#  *X Going Beyond Local: Global Graph-Enhanced Personalized News
Recommendations

Boming Yang  The University of TokyoTokyoJapan<EMAIL_ADDRESS>,
Dairui Liu  University College DublinDublinIreland<EMAIL_ADDRESS>,
Toyotaro Suzumura  The University of TokyoTokyoJapan
<EMAIL_ADDRESS>,  Ruihai Dong  University College
DublinDublinIreland<EMAIL_ADDRESS>and  Irene Li  The University of
TokyoTokyoJapan<EMAIL_ADDRESS>
(2023)

###### Abstract.

Precisely recommending candidate news articles to users has always been a core
challenge for personalized news recommendation systems. Most recent works
primarily focus on using advanced natural language processing techniques to
extract semantic information from rich textual data, employing content-based
methods derived from local historical news. However, this approach lacks a
global perspective, failing to account for users’ hidden motivations and
behaviors beyond semantic information. To address this challenge, we propose a
novel model called GLORY (Global-LOcal news Recommendation sYstem), which
combines global representations learned from other users with local
representations to enhance personalized recommendation systems. We accomplish
this by constructing a Global-aware Historical News Encoder, which includes a
global news graph and employs gated graph neural networks to enrich news
representations, thereby fusing historical news representations by a
historical news aggregator. Similarly, we extend this approach to a Global
Candidate News Encoder, utilizing a global entity graph and a candidate news
aggregator to enhance candidate news representation. Evaluation results on two
public news datasets demonstrate that our method outperforms existing
approaches. Furthermore, our model offers more diverse recommendations111Our
code is released on https://github.com/tinyrolls/GLORY.

News Recommendation, Graph Neural Network, News Modeling

††copyright: none††journalyear: 2023††copyright: acmlicensed††conference:
Seventeenth ACM Conference on Recommender Systems; September 18–22, 2023;
Singapore, Singapore††booktitle: Seventeenth ACM Conference on Recommender
Systems (RecSys ’23), September 18–22, 2023, Singapore, Singapore††price:
15.00††doi: 10.1145/3604915.3608801††isbn: 979-8-4007-0241-9/23/09††ccs:
Information systems Recommender systems

##  1\. INTRODUCTION

News Recommendation (NR) is the process of recommending news articles to users
to satisfy their need for information by optimizing the accuracy of predicting
relevance between news articles and users. Compared with making
recommendations in other domains, news recommendation is more challenging
because of the highly dynamic environment caused by the natural
characteristics of news articles, such as timeliness, novelty, etc., for
example, rapid changes occur in the relevance of news articles (Feng et al.,
2020). Therefore, unsurprisingly, content-based recommendation methods (Wu et
al., 2019c, b, a; An et al., 2019; Qi et al., 2021; Li et al., 2022b) have
proven their effectiveness. Typically, these are achieved via harnessing
various natural language processing (NLP) and machine learning (ML)
technologies to extract user interest representations by analyzing news
articles that they have read in the past, to build representations of
candidate news articles by studying their content, and then match users with
candidate news articles.

Recently, deep learning-based technologies have been developed rapidly and
brought new opportunities for enhancing content-based solutions due to their
strong ability to deal with textual data, such as news content, news title,
etc., and capture sequential dependencies in user reading history. For
example, (Wu et al., 2019c) defines a news encoder and a user encoder based on
multi-head self-attention mechanisms, which learn news representations from
titles and learn user representation from news embeddings. (Wu et al., 2019a)
acquires additional semantic information by using both word-level and view-
level attention mechanisms to the news encoder to select important words and
views for learning informative news representations. Research on modeling user
preferences has gained popularity. (Qi et al., 2021) employs a three-layer
hierarchical structure to learn user interests from the subtopic and topic
levels of news articles. Additionally, graph-based methods represent another
direction in this field. For instance, (Mao et al., 2022) utilizes a semantic
augmentation graph to enhance news items and leverages a dual-graph
interaction method to learn both candidate news and user representations.

Figure 1. An example of how global feature may help predict the candidate
news. The figure depicts three users, U1, U2, and U3, along with three
distinct historical news articles represented by green, blue, and orange
colors. Dotted blue and green candidate news articles are related to the
content of the blue and green news articles, respectively.

Whilst these solutions have achieved great success in news recommendation
tasks, such as alleviating the serious item user behaviors sparse problem by
extracting meaning representation from news content, these methods mainly
focus on the reading history of each user individually (i.e., using a single
user’s historical news click sequence to construct this user’s
representation), which lack a global border view of news articles and clicking
among multiple users, and might be insufficient for uncovering more implicit
hidden user behaviors. For example, as illustrated in Fig. 1, assume that we
want to recommend news articles for user U1. User U1’s reading list contains
only an orange news article, but at the moment, there are no news articles in
the candidate set similar to the orange one. If recommendation models merely
consider U1’s reading history, it is difficult to make recommendations for U1
in this situation. But if we can harness multiple user histories together, for
example, extracting a Global News Graph from user U3 and U2’s clicks, we may
find that there are certain relationships between orange news articles with
blue news articles and green news articles. Therefore, the model may consider
recommending dotted blue and green candidates that are similar to the blue one
and the green one respectively. The challenge is how to properly generate this
global news graph based on clicking from multiple users and integrate it with
news recommendation systems.

To address the mentioned issue, we propose a novel model, GLORY (Global-LOcal
news Recommendation sYstem). Because historical news interaction data can
provide more extensive and implicit relational information than semantic
relevance, we propose a global-aware historical news encoder, specifically,
using a global news graph to provide information on global perspectives for
historical news. At the same time, to address the user behaviors sparse
problem of candidate news, we propose a global-aware candidate news encoder,
which uses a global entity graph to provide more effective associations for
candidate news. Next, we use the multi-head self-attention mechanism to
extract user interests from historical news. The final matching score came
from the user news vector and the candidate news vector. We conducted
extensive experiments on the MIND news dataset and Adressa, and the results
showed that our model outperformed existing methods.

The main contributions of this work can be summarized as follows: 1) To the
best of our knowledge, we are the first to propose a global perspective for
constructing a homogeneous global news/entity graph in the news recommendation
domain, which enables more effective utilization of rich historical
interaction information; 2) We introduce a global-aware historical news
encoder and a global-aware candidate news encoder that leverage the global
news graph and global entity graph, respectively, to enhance the
representations of historical news and candidate news; 3) Extensive
experiments on real-world datasets demonstrate that GLORY achieves state-of-
the-art performance.

##  2\. RELATED WORK

###  2.1. Feature-based News Recommendation

Traditional recommendation methods leverage features of users or news from the
reading history. Typically, the factorization machine method is utilized to
learn information from interaction data. For instance, LibFM (Rendle, 2012)
employs factorization machines (FM) to obtain features of each news, and then
uses the learned features to conduct recommendations. DeepFM (Guo et al.,
2017), on the other hand, utilizes an FM component and a deep component to
capture more profound connection information. However, the limitation of these
approaches lies in the fact that candidate news items often have sparse or no
interaction history, making it challenging to learn effective representations.

Figure 2. An overview of the proposed GLORY model. The blue node $d_{i}$
represents historical news in a user’s behavior, while the grey node $d_{c}$
signifies candidate news $d_{c}$. The red node $e_{i}$ in the adjacent
entities denotes an entity belonging to $d_{c}$. The embedding
$\mathbf{h}^{ln}$ and $\mathbf{h}^{ln}_{c}$ denote the local news
representations, and the embedding $\mathbf{h}^{le}$ and $\mathbf{h}^{le}_{c}$
denote the local entity representations. $\mathbf{emb}_{user}$ and
$\mathbf{emb}_{cand}$ are user presentation and candidate news representation,
both used for news recommendation.

###  2.2. Neural News Recommendation

One advantage of news recommendation scenarios is the richness of textual
information. With the rapid development of NLP techniques, research has
started to focus more on content-based news recommendation systems. For
example, NAML (Wu et al., 2019a) utilizes convolutional neural networks (CNNs)
(Hughes et al., 2017) to learn news representations and gated recurrent units
(GRUs) (Chung et al., 2014) to learn user representations. NRMS (Wu et al.,
2019c), on the other hand, employs multi-head self-attention mechanisms for
both news and user modeling. Moreover, some models feature complex structures
for news and user modeling, such as HieRec (Qi et al., 2021), which uses a
three-layer hierarchical structure combined with topics and subtopics to learn
user representations; and MINER (Li et al., 2022b), which employs a poly
attention scheme with disagreement regularization and category-aware attention
weighting to obtain 32 user interest vectors.

###  2.3. Graph-based News Recommendation

NLP using graph-based techniques has emerged as a prominent field of research
and development in recent years (Wu et al., 2021a; Li et al., 2022a, 2021).
Rather than relying solely on NLP for modeling semantic information in news
text, certain approaches have embraced the use of graphs to learn news
representations. For example, GERL (Ge et al., 2020) learns text-based news
representations from news titles and topics, and learns graph-based news
representations from the user-news graph. GNewsRec (Hu et al., 2020) utilizes
a heterogeneous user-news-topic graph to discover long-term interests. User-
as-graph (Wu et al., 2021b) proposes a heterogeneous graph pooling method to
aggregate the local news graph formed by a user’s reading history. DIGAT (Mao
et al., 2022) takes advantage of a semantic augmentation graph, constructed
using semantically related methods, to enrich candidate news content.

Nonetheless, the mentioned related work, including content-based and graph-
based methods, focuses more on complex models for contextual information to
model the current user, lacking a comprehensive understanding of global news
and failing to utilize the rich global user-news interaction information
effectively. In contrast, our proposed model GLORY considers global news graph
information, capturing users’ latent behavioral patterns and enriching the
news representations beyond merely local news information.

##  3\. METHOD

Our proposed approach, GLORY, focuses on enhancing historical news
representation by utilizing a global news graph, and improving candidate news
representation through a global entity graph, as depicted in Fig. 2. First, we
learn the representation of news text and news entities from a local
perspective. Subsequently, we adopt the global-aware historical news encoder
and the global-aware entity news encoder. Lastly, we employ a concise user
encoder and a news recommendation component.

###  3.1. Problem Formulation

The click history sequence of a user $u$ can be denoted as
$\mathbf{H}_{u}=[d_{1},d_{2},...,d_{H}]$, where $H$ is the number of
historical news articles. Each news article $\mathbf{d}_{i}$ has a title,
which contains a text sequence $\mathbf{T}_{i}=[w_{1},w_{2},...,w_{T}]$
consisting of $T$ word tokens, and an entity sequence, which is denoted by
$\mathbf{E}_{i}=[e_{1},e_{2},...,e_{E}]$ consisting of $E$ entities. The
objective is to predict the level of interest $s_{u,c}$ for a given candidate
news article $d_{c}$ and user $u$, which reflects the likelihood of a clicking
action occurring between them. The recommendation task involves ranking
multiple candidate news articles based on probability scores.

###  3.2. Local Representation

First, we need to learn local news representation $\mathbf{h}^{ln}$ and local
entity representation $\mathbf{h}^{le}$ from news text and news entities. For
each user behavior, we only consider the latest $L_{his}$ clicked news from
the click history sequence $\mathbf{H_{u}}$ of user $u$ and candidate news
$d_{c}$. In this work, we use only the news title as the news text, and for
each news title, we truncate the first $L_{title}$ words as input.

Figure 3. A simple Case of Global News Graph and Global Entity Graph. Global
News Graph uses two reading sequences $[n_{1},n_{2},n_{3},n_{4}]$ and
$[n_{7},n_{5},n_{2},n_{6}]$ from user U2023 and U3407, respectively. And
Global Entity Graph shows six entities from news $n_{1},n_{2},n_{3}$, and
displays the entities $e_{1},e_{2},e_{3}$ from news $n_{1}$.

####  3.2.1. Local News Representation

The local news encoder aims to obtain local news representation from the news
text. It comprises a word embedding layer, where we use pre-trained GloVe
(Pennington et al., 2014) word embeddings as initialization, for converting a
news text from a sequence of words $[w_{1},w_{2},\ldots,w_{T}]$ into a
sequence of word embedding vectors
$\mathbf{x}_{n}=[x_{1}^{\omega};x_{2}^{\omega};\ldots;x_{T}^{\omega}]$,
followed by a text multi-head self-attention (MSA) (Vaswani et al., 2017)
layer for learning word representations $\mathbf{X}_{n}$:

(1) $\displaystyle\mathbf{X}_{n}=\text{MSA}(\mathbf{x_{n}})$
$\displaystyle=\text{Concat}(\text{head}_{1},\dots,\text{head}_{h})\cdot\mathbf{W}^{O},$
(2) $\displaystyle\text{where}\quad\text{head}_{i}$
$\displaystyle=\text{Att}(\mathbf{x}_{n}\cdot\mathbf{W}^{Q}_{i},\mathbf{x}_{n}\cdot\mathbf{W}^{K}_{i},\mathbf{x}_{n}\cdot\mathbf{W}^{V}_{i}),$
(3) $\displaystyle\text{Att}(Q,K,V)$
$\displaystyle=softmax(\frac{QK^{\top}}{\sqrt{d_{k}}})V.$

Here, Concat is the concatenation function, and $\mathbf{W}^{O}$ is a
trainable matrix used to concatenate the outputs. For each head $i$, we use
three matrix transformations $\mathbf{W}^{Q}_{i}$, $\mathbf{W}^{K}_{i}$,
$\mathbf{W}^{V}_{i}$ to map the input $\mathbf{x}_{n}$ to query, key, and
value vectors. The $\text{Att}(\cdot)$ function adopts a Scaled Dot-Product
Attention to compute a weighted sum of the value vectors, resulting in the
output vector for each head. And $d_{k}$ denotes the dimension of keys.

Since each user has multiple news in the history, we then adopt a text
attention layer to learn local news representation $\mathbf{h}^{ln}$ by
aggregating word representations:

(4)
$\mathbf{h}^{ln}=\sum_{i=1}^{T}\alpha_{i}^{\omega}x_{i}^{\omega},\quad\alpha_{i}^{\omega}=\frac{exp(\mathbf{q}_{\omega}^{\top}\tanh(\textbf{W}^{\omega}\cdot\mathbf{X}_{n}^{i}))}{\sum_{j=1}^{T}exp(\mathbf{q_{\omega}^{\top}}\tanh(\textbf{W}^{\omega}\cdot\mathbf{X}_{n}^{j}))},$

where $\textbf{W}^{\omega}$ is a trainable matrix, and $\mathbf{q}_{\omega}$
is the query vector. And $\mathbf{X}_{n}^{i}$ denotes the $i$-th word
representation of $\mathbf{X}_{n}$.

####  3.2.2. Local Entity Representation

In addition, we employ a local entity encoder to learn local entity
representations. The process begins with an entity embedding layer that merges
information from the knowledge graph, where we use a pre-trained TransE
(Bordes et al., 2013) entity embedding based on WikiData
222https://www.wikidata.org/wiki/Wikidata:MainPage, to convert a sequence of
entities into a sequence of entity embedding vectors
$\mathbf{x}_{e}=[x_{1}^{e};x_{2}^{e};\ldots;x_{E}^{e}]$. Subsequently, we use
an entity self-attention network to learn entity representations
$\mathbf{X}_{e}$ and an attention network to aggregate multiple entities and
obtain the local entity representation $\mathbf{h}^{le}$, as in Eq 4.

###  3.3. Global-aware Historical News Encoder

To go beyond the local, we introduce a novel global-aware historical news
encoder to merge global and local information.

####  3.3.1. Global News Graph

The Global News Graph is used to summarize users’ reading histories. On the
left part of Fig. 3, we show a directed Global News Graph, denoted as
$G_{n}=(V_{n},E_{n})$, where $V_{n}$ and $E_{n}$ represent the sets of news
articles and edges, respectively. To construct this graph, we collect the
edges from each user’s reading history in the training dataset. Specifically,
a directed edge $(v_{i},v_{j})$ is added to the graph to indicate that news
item $v_{j}$ was read immediately after $v_{i}$ by the same user. The edge
weight is determined by the frequency of this occurrence from all reading
history.

####  3.3.2. Graph Encoder

For each user history, we extract the subgraph
$G_{sub}^{u}=(V_{sub}^{u},E_{sub}^{u})$ from the global news graph to capture
a global perspective on a specific user’s interests. For each news in
$\mathbf{H_{u}}$ of user $u$, we select news neighbors of multiple hops from
the global news graph. For each hop, we select the top $M_{n}$ neighbors based
on the edge weight. We adopt graph neural networks (GNN) (Li et al., 2015) to
encode the subgraph to obtain global news embedding $\mathbf{h}^{gn}$:

(5) $\mathbf{h}^{gn}=\textrm{GNN}(\mathbf{h}^{ln}).$

While there are multiple choices for the graph encoder, here we apply the
gated graph neural network (GGNN) (Li et al., 2017), which employs a gated
recurrent unit (GRU) (Cho et al., 2014) to capture sequence-based hidden
behavior information:

(6) $\displaystyle\mathbf{h}^{(0)}=\mathbf{h}^{ln},$ (7)
$\displaystyle\mathbf{m}_{i}^{(l+1)}=\sum_{j\in\mathcal{N}(i)}W_{g}\cdot\mathbf{h}_{j}^{(l)},$
(8)
$\displaystyle\mathbf{h}_{i}^{(l+1)}=\textrm{GRU}(\mathbf{m}_{i}^{(l+1)},\mathbf{h}_{i}^{(l)}).$

We initialize $\mathbf{h}^{(0)}$ with $\mathbf{h}^{ln}$. $N_{i}$ represents
the set of neighboring nodes for node $i$ in subgraph $G_{sub}^{u}$, $l$
denotes layer number, and $W_{g}$ is a trainable matrix.

####  3.3.3. Historical News Aggregator

For each historical news article $d_{k}$, we have the local news
representation $\mathbf{h}^{ln}$, the local entity representation
$\mathbf{h}^{le}$ and the global title representation $\mathbf{h}^{gn}$. We
now utilize an attention pooling network to learn the historical news
representation $\mathbf{n}_{k}$ by aggregating these three representations
$\mathbf{h}^{k}=[\mathbf{h}^{ln};\mathbf{h}^{le};\mathbf{h}^{gn}]$:

(9)
$\mathbf{n}_{k}=\sum_{i=1}^{3}\alpha_{i}^{h}\mathbf{h}_{i}^{k},\quad\alpha_{i}^{h}=\frac{exp(\mathbf{q}_{h}^{\top}\tanh(\textbf{W}^{h}\cdot\mathbf{h}^{k}_{i}))}{\sum_{j=1}^{3}exp(\mathbf{q_{h}^{\top}}\tanh(\textbf{W}^{h}\cdot\mathbf{h}^{k}_{j}))}.$

where $\textbf{W}^{h}$ is a trainable matrix, and $\mathbf{q}_{h}$ is the
query vector. And $\mathbf{h}_{i}^{k}$ denotes the $i$-th concatenated
representation of $\mathbf{h}^{k}$.

###  3.4. User Encoder

Next, we briefly introduce the architecture of the user encoder in GLORY.
Following (Wu et al., 2019c), after we obtain news embedding $\mathbf{n}_{k}$
of each news in a given user reading history $H_{u}$, we employ a multi-head
attention layer and an attention pooling layer to finally learn the user
representation $\textbf{emb}_{user}$, similar as Eq. 4.

###  3.5. Global-aware Candidate News Encoder

As a vast number of news articles are generated daily, candidate news often
lacks user interaction history. Therefore, we can not only enhance historical
news using the global news graph but also improve candidate news using a
similar global entity graph. Consequently, we propose a novel global-aware
candidate news encoder.

####  3.5.1. Global Entity Graph

In order to construct a global entity graph, we employed a similar approach to
that used in the global news graph but adopted an undirected graph. Shown in
Fig. 3, the global entity graph is represented as $G_{e}=(V_{e},E_{e})$, where
$V_{e}$ and $E_{e}$ are the sets of entities and edges. Entity edges are
derived from each user’s reading history on the training dataset. An
undirected edge $(v_{i},v_{j})$ implies that $v_{j}$ is the entity of the last
news article and $v_{i}$ is the entity of the subsequent news article. The
edge weight of $(v_{i},v_{j})$ is determined by the count of news edge
occurrences.

####  3.5.2. Global Entity Encoder

Due to the significantly smaller number of entities compared to the number of
news articles, the average degree of nodes in the global entity graph is much
higher. Therefore, it is more convenient and concise to use adjacent entities
directly instead of employing entity subgraphs. Similarly, we select the top
$M_{e}$ neighbor entities for each entity in candidate news $d_{c}$ based on
edge weights and use the same entity embedding layer as the local-view entity
encoder, the final embedding represented as
$\mathbf{x}_{c}=[x_{1}^{c},x_{2}^{c},...,x_{E*M_{e}}^{c}]$. Similarly, as in
Eq.4, we utilize a $MSA$ to learn the entity token representations
$\mathbf{X}_{c}=\text{MSA}(\mathbf{x}_{c})$ and an attention network to
aggregate the adjacent entity representations $\mathbf{h}_{c}^{ge}$.

####  3.5.3. Candidate News Aggregator

For each candidate news article $d_{c}$, we have the local news representation
$\mathbf{h}_{c}^{ln}$, the local entity representation $\mathbf{h}_{c}^{le}$
and the global entity representation $\mathbf{h}_{c}^{ge}$. We use an
attention pooling network to learn the candidate news representation
$\mathbf{emb}_{cand}$ by aggregating these three representations
$\mathbf{h}^{c}=[\mathbf{h}_{c}^{ln};\mathbf{h}_{c}^{le};\mathbf{h}_{c}^{ge}]$,
as in Eq. 9.

###  3.6. News Recommendation

In accordance with the prior work (Wu et al., 2019b), we employ the negative
sampling technique during our training process. For every behavioral session
in the training dataset, each clicked candidate news is denoted as a positive
sample $n_{i}^{+}$, while we randomly select $K_{neg}$ non-clicked candidate
news within the same session as negative samples
$[n_{i}^{1},n_{i}^{2},...,n_{i}^{K_{neg}}]$. Subsequently, we estimate the
click score for each news item as
$\mathbf{\hat{y}_{i}}=[\hat{y}_{i}^{+},\hat{y}_{i}^{1},\hat{y}_{i}^{2},...,\hat{y}_{i}^{K_{neg}}]$,
by conducting an inner product of user embedding $\mathbf{emb}_{user}$ and all
samples $\mathbf{emb}_{cand}$, which contains one positive sample and
$K_{neg}$ negative samples. Finally, we optimize the positive samples log-
likelihood loss $\mathcal{L}_{NCE}$ over the training dataset during model
training:

(10)
$\hat{\mathbf{y}_{i}}=softmax(\mathbf{emb}_{user}\cdot\mathbf{emb}_{cand}),$
(11)
$\mathcal{L}_{NCE}=-\sum_{i=1}\log\frac{exp(\hat{\mathbf{y}}_{i}^{+})}{exp(\hat{\mathbf{y}}_{i}^{+})+\sum_{j=1}^{K_{neg}}exp(\hat{\mathbf{y}}_{i}^{j})}.$

##  4\. Experimental Setup

###  4.1. Datasets

We conduct extensive experiments on two real-world datasets to evaluate the
performance of our model. The first one is the MIND dataset(Wu et al., 2020),
which was collected from anonymized behavior logs of Microsoft
News333https://news.microsoft.com website. The full version of MIND (MIND-
large) includes a million sampled users, who had at least 5 news clicks during
6 weeks from October 12 to November 22, 2019. In addition, a small version of
MIND (MIND-small) was also released by sampling 50,000 users and their
behavior logs. Another dataset is Adressa (Gulla et al., 2017), which was
collected from Neiwegen News Website Adresseavisen444https://www.adressa.no/,
and here we use the light version which includes 1 week (from 1 January to 7
January 2017). Key statistics of datasets are shown in Tab. 1.

Table 1. Dataset statistics. | MIND-small | MIND-large | Adressa
---|---|---|---
# News | 65,238 | 161,013 | 14,732
# Users | 50,000 | 1,000,000 | 537,627
# Clicks | 347,727 | 24,155,470 | 2,527,571

###  4.2. Evaluation metrics

Following the setting from (Wu et al., 2020), we evaluate recommendation
performance via four metrics, Area under the ROC Curve (AUC), Mean Reciprocal
Rank (MRR), and normalized discounted cumulative gain (nDCG@N) with $N=5,10$.

Table 2. Evaluation performance of all baselines on MIND. There are some missing values because we reuse the results from existing works: $\ast$ is taken from(Li et al., 2022b); $\dagger$ is taken from(Mao et al., 2022); $\ddagger$ is taken from (Wu et al., 2021b).  Method | MIND-small | MIND-large
---|---|---
AUC | MRR | nDCG5 | nDCG10 | AUC | MRR | nDCG@5 | nDCG@10
LibFM | 59.74 | 26.33 | 27.95 | 34.29 | 61.85 | 29.45 | 31.45 | 37.13
DeepFM | 59.89 | 26.21 | 27.74 | 34.06 | 61.87 | 29.3 | 31.35 | 37.05
DKN | 62.90 | 28.37 | 30.99 | 37.41 | 64.07 | 30.42 | 32.92 | 38.66
NPA | 64.65 | 30.01 | 33.14 | 39.47 | 65.92 | 32.07 | 34.72 | 40.37
NAML | 66.12 | 31.53 | 34.88 | 41.09 | 66.46 | 32.75 | 35.66 | 41.40
LSTUR | 65.87 | 30.78 | 35.15 | 40.15 | 67.08 | 32.36 | 35.15 | 40.93
NRMS | 65.63 | 30.96 | 34.13 | 40.52 | 67.66 | 33.25 | 36.28 | 41.98
FIM | 65.34 | 30.64 | 33.61 | 40.16 | 67.87 | 33.46 | 36.53 | 42.21
HieRec | 67.95 | 32.87 | 36.36 | 42.53 | 69.03 | 33.89 | 37.08 | 43.01
MINER w/o PLM$\ast$ | 68.07 | 32.93 | - | 42.62 | - | - | - | -
GERL | 65.27 | 30.10 | 32.93 | 39.48 | 68.10 | 33.41 | 36.34 | 42.03
GNewsRec | 65.54 | 30.27 | 33.29 | 39.80 | 68.15 | 33.45 | 36.43 | 42.10
User-as-Graph$\ddagger$ | - | - | - | - | 69.23 | 34.14 | 37.21 | 43.04
DIGAT w/o PLM$\dagger$ | 67.82 | 32.65 | 36.25 | 42.49 | - | - | - | -
GLORY(ours) | 68.15 | 32.97 | 36.47 | 42.78 | 69.54 | 34.03 | 37.92 | 44.19

###  4.3. Baselines

We mainly select three groups of baselines to compare:

Feature-based Methods (1) LibFM (Rendle, 2012), a widely used recommendation
method that uses factorization machines to predict user preferences for news
articles based on their features; (2) DeepFM (Guo et al., 2017), a neural
network-based recommendation method that combines factorization machines with
deep learning to improve recommendation accuracy for news articles.

Neural Recommendation Methods: (1) DKN (Wang et al., 2018), using a knowledge-
aware CNN to learn news representation from both news text and knowledge
entities; (2) NPA (Wu et al., 2019b), using personalized attention network to
learn news and user representation; (3) NAML (Wu et al., 2019a), using the
multi-view attention network learn representation based on different news
article features; (4) LSTUR (An et al., 2019), jointly modeling users’ long-
term and short-term interests by a GRU network; (5) NRMS (Wu et al., 2019c),
using the multi-head self-attention network to learn user and news
representation; (6) FIM (Wang et al., 2020), utilizing hierarchical
representations of news articles through stacked dilated convolutions to
perform fine-grained interest; (7) HieRec (Qi et al., 2021): using a three-
level hierarchical structure to represent each user’s diverse and multi-
grained interest. (8) MINER w/o PLM (Li et al., 2022b): using a poly attention
scheme with disagreement regularization to model multiple user interests. We
select the version without the pre-trained language model (PLM) to compare
fairly.

Graph-based Methods: (1) GERL (Ge et al., 2020), using a bipartite graph and
combines transformer architecture with a graph attention network to enhance
news and user representations; (2) GNewsRec (Hu et al., 2020), utilizing a
heterogeneous graph to model user-news-topic interactions, and employs graph
neural networks to learn high-order user and news representations; (3) User-
as-Graph (Wu et al., 2021b), using a personalized heterogeneous graph built
from user behaviors and a novel heterogeneous graph pooling method; (4) DIGAT
(Mao et al., 2022), using news- and user-graph channels and a dual-graph
interaction process to match news-user representations effectively. Similarly,
we choose the version without PLM.

###  4.4. Experiment Settings

Following the previous work (Wu et al., 2019b), we set the most recently
clicked news article of a user as $L_{his}=50$, and the maximum title length
as $L_{title}=30$. The number of maximum used entities of each news is set to
be $L_{entity}=5$, and the number of neighboring news $M_{n}$, and hops $K$ in
the news sub-graph are 8 and 2, respectively. The number of neighboring
entities $M_{e}$ in adjacency entities is 10. We set the number of GGNN layers
as 3. We adopt 300-dimensional pre-trained GloVe word embeddings and
100-dimensional pre-trained TransE entity embeddings for MIND datasets
initialization. And the historical and candidate news representations are
400-dimensional. As for the optimizer, we choose Adam (Kingma and Ba, 2014) as
our optimization algorithm, with a learning rate $2e^{-4}$ and linearly
decayed with 10% warm-up steps. And the negative sampling rate $K_{neg}$ is
set to 4. All hyper-parameters were selected according to the result of the
validation set. For the Adressa dataset, the difference is that we only use
news titles, with randomly initialized word embeddings and a learning rate of
$1e^{-5}$.

##  5\. RESULTS

###  5.1. Main Result

Tab. 2 shows the performance comparison between GLORY and the baselines on two
settings of the MIND dataset. The tabulated values are expressed as
percentages without the symbol $\%$. The overall best and baseline best
results are boldfaced and underlined respectively.

AUCnDCG<EMAIL_ADDRESS>Figure 4. Comparison of graph encoders, results on MIND-small.

MIND We have several reservations from Tab. 2: 1) The outcomes of neural news
recommendation methods surpass those of feature-based methods across all
cases, thereby highlighting the efficacy of neural networks in modeling news
and users based on implicit semantic representation rather than manually
created features. 2) While some baseline methods apply news topics and
categories as auxiliary information to improve news encoding (i.e., HieRec),
or design complex architecture to encode users (i.e., MINER w/o PLM constructs
32 interest vectors using a polyhedral interest system for users), they focus
on a local perspective of the historical news of a given user. Our GLORY model
integrates global features and outperforms these baselines. Although DIGAT w/o
PLM employs a semantically enhanced graph to enable the model to learn beyond
historical news, it is still based on semantic relevance and does not learn
latent behavioral information from other users’ interactions. In contrast,
GLORY, even with simply attention-based user interest encoding, can achieve
the best performance, and the global graph information provides significant
assistance in learning news encoding. 3) Both GERL and GNewsRec employ graph-
based methods to model user-news interactions. However, the bipartite
interaction-based approach faces difficulties in the test dataset,
particularly when candidate news items are novel and lack interaction history.
In contrast, our approach enhances historical news by incorporating
information from the global news graph, thereby augmenting user interests. We
also leverage a global entity graph to strengthen the relevance of candidate
news beyond just semantics.

Table 3. Evaluation performance of several baselines on Adressa dataset. All methods only utilize the news titles. Method | Adressa
---|---
AUC | MRR | nDCG@5 | nDCG@10
NPA | 71.89 | 38.62 | 38.76 | 45.72
LSTUR | 73.17 | 39.47 | 39.79 | 47.21
NAML | 73.14 | 38.10 | 38.56 | 46.13
NRMS | 72.33 | 41.08 | 41.92 | 48.57
GLORY | 74.31 | 42.29 | 43.37 | 49.51

Adressa Tab. 3 demonstrates the performance of GLORY and several baselines on
the Adressa dataset. Since we only use news titles in Adressa, without
categories and entities, we select several open-source methods that rely
solely on titles. We observe that even without explicit entity information,
GLORY achieves the best performance due to the enhancement provided by the
global news graph. This suggests that historical news interactions from other
users can offer additional hidden behavioral information, which helps uncover
relevance beyond semantics. LSTUR outperforms NPA, showing that the GRU method
has a significant ability to detect the sequence of reading history.
Furthermore, NRMS achieves the second-best MRR, nDCG@5, and nDCG@10
performances, indicating that multi-head self-attention effectively selects
important news to learn more informative news and user representations. Our
GLORY model takes advantage of both aspects by learning news representation
through multi-head self-attention and capturing sequence information from the
global news graph. Consequently, we use the same dataset content but better
utilize the information to achieve improved results.

###  5.2. Ablation Study

We aim to enhance our understanding of the effectiveness of each component in
GLORY by conducting ablation studies on degraded models. Specifically, we
conduct four experiments on entity and news graph: 1) full model; 2) w/o
g-news: without global news graph enhanced representation; 3) w/o g-entity:
without global entity graph enhanced representation; 4) w/o g-news/entity:
without both aforementioned components. In each experiment, all other settings
remained unchanged as in the full model, with only one component being either
replaced or removed. We evaluate those experiments on MIND-small. The results
of these experiments are presented in Tab. 4.

Table 4. Effects of different GLORY components, result on MIND-small. | AUC | MRR | nDCG@5 | nDCG@10
---|---|---|---|---
full model | 68.15 | 32.97 | 36.47 | 42.78
w/o g-news | 67.53 | 32.39 | 35.89 | 42.10
w/o g-entity | 67.77 | 32.57 | 36.09 | 42.38
w/o g-news/entity | 67.28 | 32.00 | 35.38 | 41.77

The full model performs the best, but the individual components also play a
critical role in achieving optimal results. The results showed that the global
news graph has the most significant impact. For instance, without the global
news graph, the AUC metric dropped from 68.15 to 67.53, demonstrating the
effectiveness of these graph components in enhancing news recommendations.
These global graphs provide hidden information beyond local semantics and can
effectively improve the performance of news recommendations.

Graph Encoder Variations We then study various graph encoders Eq 5 in this
section. We compare three popular graph models with GGNN in GLORY: Graph
Convolutional Network (GCN) (Kipf and Welling, 2016), Graph Attention Network
(GAT) (Velickovic et al., 2017), and GraphSAGE (Hamilton et al., 2017). In
Fig. 4, we report their performance on the MIND-small. We observe that GGNN
significantly outperforms the other graph models, as the GGNN utilizing GRU is
capable of learning the hidden sequential information underlying users’ click
behavior.

###  5.3. Analysis on Hyperparameters

48121620$67.2$$67.4$$67.6$$67.8$$68$$68.2$$M_{n}$AUC$35$$37.5$$40$$42.5$$45$nDCGnDCG@5nDCG@10AUC
(a) Impact of news neighbors $M_{n}$ (on fixed $K=2,M_{e}=10$).

5101520$67.2$$67.4$$67.6$$67.8$$68$$68.2$$M_{e}$AUC$35$$37.5$$40$$42.5$$45$nDCGnDCG@5nDCG@10AUC
(b) Impact of adjacent entities $M_{e}$ (on fixed $K=2,M_{n}=8$).

Figure 5. Recommendation performance of GLORY with different $M_{n}$ and
$M_{e}$ settings.

We investigate three key hyperparameters, i.e., the number of news neighbors
$M_{n}$ and hops $K$, and the number of adjacent entities $M_{e}$. We plot the
results on MIND-small in Fig. 5 to show the effect of $M_{n}$, $K$, and
$M_{e}$ settings. As shown in Fig. 5(a), with a fixed $K=2$ and $M_{n}=8$, the
performance of GLORY is optimal when $M_{e}=10$, suggesting that adjacent
entities indeed help uncover more related information, but an excessive number
of such entities introduce too much noise, thereby reducing the model’s
performance. Similarly, in Fig. 5(b), when $K=2$ and $M_{e}=10$ are fixed,
GLORY performs best with $M_{n}=8$, revealing that the model’s performance
does not continuously improve with an increasing number of news node neighbors
while extracting global news subgraphs. This may be because having too many
news neighbors exceeds the range of effective associations, leading to weakly
related news interfering with the accurate information in the subgraph.
Additionally, we test the model’s performance using various values of $K$. The
findings presented in Fig. 6 demonstrate that a depth of $K=2$ is optimal;
Subgraphs with insufficient depth fail to encompass the entirety of the
reading path, whereas overly deep subgraphs introduce excessive noise due to
their larger scale and consume excessive time.

1-hop2-hop3-hop$67.2$$67.4$$67.6$$67.8$$68$$68.2$AUC$35$$37.5$$40$$42.5$$45$nDCGnDCG@5nDCG@10AUC
(a) Impact of different hops ($K$) (on fixed $M_{e}=10,M_{n}=8$).

1-hop2-hop3-hop$0$$1$$2$$3$$4$$5$$6$$7$Run-Time(relative to 1-hop) (b) Impact
of different hops ($K$) (on fixed $M_{e}=10,M_{n}=8$).

Figure 6. Performance of GLORY with different $K$ settings.

###  5.4. Impact of Graph Construction

Table 5. Effects of different Graph Construction on Global Entity Graph. The edge num. indicates the number of directed edges, and an undirected edge is counted as two directed edges. Method | edge num. | AUC | MRR | nDCG@5 | nDCG10
---|---|---|---|---|---
Intra-news & Inter-news Directed | 522,595 | 67.86 | 32.67 | 36.13 | 42.42
Intra-news & Inter-news Undirected | 870,362 | 67.73 | 32.59 | 36.13 | 42.40
Inter-news Directed | 506,507 | 67.75 | 32.59 | 36.04 | 42.35
Inter-news Undirected | 841,722 | 68.15 | 32.97 | 36.47 | 42.78

####  5.4.1. Global News Graph

We investigate the effects of different global news graph construction methods
as described in Sec 3.3.1, particularly focusing on the connections between
the edges of different news articles.

We conducted three experiments to analyze the effectiveness of the global news
graph construction. These experiments are as follows: 1) Dir-Seq (Directed
Sequential): In this experiment, the historical news of each user is treated
as a directed sequence, in which the preceding and succeeding news articles
are connected in chronological order; 2) Undir-Seq (Undirected Sequential):
This experiment is based on the previous experiment, with all directed edges
transformed into undirected edges; 3) (Co-occur) Co-occurrence: In this
experiment, undirected edges are created between every pair of news articles
that appear in the same history sequence. We show the results on MIND-small in
Tab. 6.

Table 6. Effects of different Graph Construction on Global News Graph. Methods | AUC | MRR | nDCG@5 | nDCG@10
---|---|---|---|---
Dir-Seq | 68.15 | 32.97 | 36.47 | 42.78
Undir-Seq | 67.73 | 32.64 | 36.07 | 42.38
Co-oc | 67.76 | 32.47 | 35.95 | 42.23

The experimental results demonstrate that the directed sequential method
surpasses the other two methods in performance. The undirected graphs, which
include the undirected sequential and co-occurrence compositions, are unable
to utilize the user’s reading sequence effectively. Furthermore, the
undirected sequential method performs better than the co-occurrence
composition in three metrics. This may further show that the adjacent reading
sequence, either directed or undirected, may play an important role, and the
co-occurrence graph ignores such information.

Figure 7. Case study based on a sampled impression. The top table displays the
complete reading history of a sampled user, which includes three news
articles. The middle table showcases relevant news articles selected from the
subgraph, which is extracted from the global news graph based on the nodes of
the news articles in the user’s reading history. The bottom table presents the
candidate news articles chosen by the user. The main and subcategories are
only for reference. The red font and gray arrows indicate that the content in
the candidate news articles can be found to have similar counterparts in the
subgraph.

####  5.4.2. Global Entity Graph

Additionally, for the global entity graph in Sec 3.5.1, we primarily study the
connection methods based on two aspects: intra-news entity connections and
inter-news entity connections. In this case, we use the default directed
sequential composition for the global news graph. Here are four different
global entity graph connection methods: 1) Intra-news & Inter-news Directed:
connects all entities within each news article, and entities between different
news articles are connected in a directed manner; 2) Intra-news & Inter-news
Undirected: connects all entities within each news article, and entities
between different news articles are connected in an undirected manner; 3)
Inter-news Directed: only entities between different news articles are
connected in a directed manner; 4) Inter-news Undirected: only entities
between different news articles are connected in an undirected manner. The
performance on MIND-small is shown in Tab. 5.

Based on the table, it appears that the model performs better without intra-
news relations. One explanation could be that entities that frequently appear
together in the same news article are often closely related in the knowledge
graph. Therefore, intra-news relations may primarily add semantically related
neighboring entities, while without them, the model can capture more diverse
and global associations. Moreover, the use of inter-news undirected relations
can help entities with fewer connections to have more neighboring entities,
thus improving their representation in the model.

###  5.5. Recommendation Diversity

In this section, we evaluate the recommendation diversity of GLORY and NRMS
using the intra-list average distance (ILAD@N) and the intra-list minimum
distance (ILMD@N) as metrics, with $N={5,10}$. These metrics are commonly used
to measure the average and minimum distances between recommendation items
(Zhang and Hurley, 2008; Chen et al., 2017). Following (Wu et al., 2022), we
use average GloVe word embeddings as semantic embeddings, instead of news
embeddings learned by the model because they cannot be compared across models.
The results obtained on MIND-small are presented in Tab. 7, which clearly
demonstrates the effectiveness of GLORY in improving recommendation diversity.
The NRMS model only recommends news based on local features learned from the
historical news of the current user, while GLORY leverages global information
beyond semantics to provide a wider range of content diversity. This makes
GLORY particularly effective in enhancing recommendation diversity. By taking
a more holistic approach to content analysis, GLORY is able to provide a
richer and more varied set of recommendations, leading to a better user
experience.

Table 7. Comparison of recommendation diversity on MIND-small. Model | ILAD@5 | ILMD@5 | ILAD@10 | ILMD@10
---|---|---|---|---
NRMS | 0.3189 | 0.1913 | 0.3183 | 0.1486
GLORY | 0.3226 | 0.2167 | 0.3329 | 0.1848

##  6\. Case Study

Finally, we conduct a case study to examine the superior performance of GLORY
specifically. We compare GLORY with NRMS because using the same structure for
the user encoder allows us to showcase the excellence of our news modeling
better. In Fig. 7, we present a random sample from the MIND-small test
dataset, where the corresponding user has three history news varies from
categories news-us to finance, while the candidate news the user read is about
National Football League (NFL). Merely analyzing the user’s interests
semantically, NRMS is insufficient to uncover the association between
historical news and the selected candidate news. In contrast, our model,
GLORY, is able to identify several neighbor news articles related to NFL in
the global news graph. Furthermore, GLORY recognizes that the entity ”NFL” in
the candidate news article was highly relevant to ”Cowboys”, which is an NFL
team. Consequently, GLORY ranks the selected candidate news article in the
first position, while NRMS ranks it at the 38th position.

##  7\. Limitations

Furthermore, this paper explores the limitations of our approach. First, our
approach faces an efficiency issue during training as GLORY needs to acquire
and process global information from both the global news graph and global
entity graph for each sample. Consequently, this leads to increased memory and
time requirements during training, compared to using only local information.
Secondly, our approach trains and validates using two public news datasets
containing click data from limited time periods, this restricts us to use a
static global graph and precludes testing on dynamically changing real-world
data.

##  8\. Conclusion and Future Work

This paper presents GLORY, a novel news recommendation system that utilizes
global graphs to improve news modeling. We propose the global news graph to
capture hidden behavior and patterns of all users’ reading history. This
approach addresses the limitations of relying solely on semantic news
modeling. Additionally, we employ the global entity graph to uncover deeply
hidden associations between candidate news articles and historical news
articles. Our approach enhances news recommendations by leveraging both global
and local information. We use historical news aggregator and candidate news
aggregator to fuse local embeddings and global embeddings, enriching the
implied content of news representations. Finally, we incorporate a concise
multi-head self-attention-based user encoder. Through a series of experiments
on two datasets, we reveal that our model significantly outperforms all other
state-of-the-art models and validate the contributions of different components
to the model. As for future work, we will continue exploring dynamic global
graphs that consider the freshness of news items and user behaviors, making
them suitable for real-time online recommendation systems.

###### Acknowledgements.

This research is partially supported by JSPS KAKENHI Grant Numbers JP21K21280
and JP23H03408. It is also partially supported by the Initiative on
Recommendation Program for Young Researchers and Woman Researchers,
Information Technology Center, The University of Tokyo. Additionally, the
research is also supported by Foundation for Computational Science (FOCUS)
Establishing Supercomputing Center of Excellence, Japan through RIKEN Center
for Computational Science (R-CCS). This research was conducted with the
financial support of Science Foundation Ireland 12/RC/2289_P2 at Insight the
SFI Research Centre for Data Analytics at University College Dublin. We thank
the anonymous reviewers for their insightful comments.

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