Graph neural news recommendation with long-term and short-term interest modeling

Hu, Linmei; Li, Chen; Shi, Chuan; Yang, Cheng; Shao, Chao

doi:10.1016/j.ipm.2019.102142

Cited by 163 publications

(68 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e above observation has proved that aggregating the features of too many neighbors will increase the noise in the user's representation, which is described in Section 4.2. Other related studies have empirically found similar trends, with the best layer size set as L � 2 [32,38].…”

Section: Ablation Experimentssupporting

confidence: 60%

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Zhao

Xiong

et al. 2020

Complexity

View full text Add to dashboard Cite

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

show abstract

Section: Ablation Experimentssupporting

confidence: 60%

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Zhao

Xiong

et al. 2020

Complexity

View full text Add to dashboard Cite

show abstract

“…erefore, the user preference changes need to be fully recognized to ensure the effectiveness of recommendations. Generally, the user preferences can be divided into long-term preferences and short-term preferences [18,19,22,23]. e former characterizes the user general interests that are relatively stable or that change slowly over time, whereas the latter usually refers to the user temporal interests, and they can be easily influenced by a variety of factors, such as user instant demands, recent interests, and global mainstream trends in a short period of time [18].…”

Section: User Preferences Changesmentioning

confidence: 99%

“…Recently, the user long-term and short-term preferences have drawn attention from research to obtain more precise and accurate user preference profiles and improve the recommendation performance [18,22,23,45]. For example, Tan and Liu [22] incorporated an attention mechanism into a recurrent neural network to capture the user preference changes and model the user long-term and short-term preferences.…”

Section: User Preferences Changesmentioning

confidence: 99%

“…Some studies examine the user preference changes (e.g., [20,21]) and explain the role of long-term and short-term preferences in recommendations (e.g., [22,23]), but there is a limited focus on the user preference dynamics. It can be argued that without drawing enough attention to the user preferences dynamics, the recommendation performance could be limited or dropped as recommendations that have been relevant in the past might not meet users' preferences at present because the users have changed their preferences [24,25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Time‐Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Huang

Stakhiyevich

2021

Complexity

View full text Add to dashboard Cite

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

show abstract

“…In recent years, several news recommendation methods based on deep learning techniques are proposed (Okura et al 2017;Khattar et al 2018;Wang et al 2018;Wu et al 2019c;An et al 2019;Wu et al 2019a, d, e;Ge et al 2020;Hu et al 2020). For example, Okura et al (2017) proposed to learn representations of users from the representations of their browsed news using a GRU network.…”

Section: Related Workmentioning

confidence: 99%

Neural news recommendation with negative feedback

Huang

et al. 2020

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model user interest. However, news click may not necessarily reflect user interests because users may click a news due to the attraction of its title but feel disappointed at its content. The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest. Thus, incorporating the negative feedback inferred from the dwell time of news reading can improve the quality of user modeling. In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback. We propose to distinguish positive and negative news clicks according to their reading dwell time, and respectively learn user representations from positive and negative news clicks via a combination of Transformer and additive attention network. In addition, we propose to compute a positive click score and a negative click score based on the relevance between candidate news representations and the user representations learned from the positive and negative news clicks. The final click score is a combination of positive and negative click scores. Besides, we propose an interactive news modeling method to consider the relatedness between title and body in news modeling. Extensive experiments on real-world dataset validate that our approach can achieve more accurate user interest modeling for news recommendation.

show abstract

Graph neural news recommendation with long-term and short-term interest modeling

Cited by 163 publications

References 27 publications

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

A Time‐Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Neural news recommendation with negative feedback

Contact Info

Product

Resources

About