GLoCIM: Global-view Long Chain Interest Modeling for news recommendation

• URL: http://arxiv.org/abs/2408.00859v2
• Date: Tue, 24 Sep 2024 16:54:35 GMT
• Title: GLoCIM: Global-view Long Chain Interest Modeling for news recommendation
• Authors: Zhen Yang, Wenhui Wang, Tao Qi, Peng Zhang, Tianyun Zhang, Ru Zhang, Jianyi Liu, Yongfeng Huang,
• Abstract summary: Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. Recent efforts have primarily focused on extracting local subgraph information in a global click graph constructed by the clicked news sequence of all users. We propose a Global-view Long Chain Interests Modeling for news recommendation (GLoCIM), which combines neighbor interest with long chain interest distilled from a global click graph.
• Score: 59.3925442282951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interest to match candidate news. Recent efforts have primarily focused on extracting local subgraph information in a global click graph constructed by the clicked news sequence of all users. Howerer, the computational complexity of extracting global click graph information has hindered the ability to utilize far-reaching linkage which is hidden between two distant nodes in global click graph collaboratively among similar users. To overcome the problem above, we propose a Global-view Long Chain Interests Modeling for news recommendation (GLoCIM), which combines neighbor interest with long chain interest distilled from a global click graph, leveraging the collaboration among similar users to enhance news recommendation. We therefore design a long chain selection algorithm and long chain interest encoder to obtain global-view long chain interest from the global click graph. We design a gated network to integrate long chain interest with neighbor interest to achieve the collaborative interest among similar users. Subsequently we aggregate it with local news category-enhanced representation to generate final user representation. Then candidate news representation can be formed to match user representation to achieve news recommendation. Experimental results on real-world datasets validate the effectiveness of our method to improve the performance of news recommendation.

Related papers

• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations [12.783388192026855]
  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, which includes a global news graph and employs gated graph neural networks to enrich news representations. We extend this approach to a Global Candidate News, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation.
  arXiv  Detail & Related papers  (2023-07-13T06:25:22Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• Self-Supervised Hypergraph Transformer for Recommender Systems [25.07482350586435]
  Self-Supervised Hypergraph Transformer (SHT) Self-Supervised Hypergraph Transformer (SHT) Cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph.
  arXiv  Detail & Related papers  (2022-07-28T18:40:30Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• GLIMG: Global and Local Item Graphs for Top-N Recommender Systems [12.631785780195996]
  We propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs) By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.
  arXiv  Detail & Related papers  (2020-07-28T06:50:28Z)
• Graph Enhanced Representation Learning for News Recommendation [85.3295446374509]
  We propose a news recommendation method which can enhance the representation learning of users and news. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors.
  arXiv  Detail & Related papers  (2020-03-31T15:27:31Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.