Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation

By Xiaowen Huang, Quan Fang, Shengsheng Qian, Jitao Sang, Yan Li, Changsheng Xu. 2019

117 citations recorded by the source corpus.

Compared with the traditional recommendation system, sequential recommendation holds the ability of capturing the evolution of users' dynamic interests. Many previous studies in sequential recommendation focus on the accuracy of predicting the next item that a user might interact with, while generally ignore providing explanations why the item is recommended to the user. Appropriate explanations are critical to help users adopt the recommended item, and thus improve the transparency and trustworthiness of the recommendation system. In this paper, we propose a novel Explainable Interaction-driven User Modeling (EIUM) algorithm to exploit Knowledge Graph (KG) for constructing an effective and explainable sequential recommender. Qualified semantic paths between specific user-item pair are extracted from KG. Encoding those semantic paths and learning the importance scores for each path provides the path-wise explanation for the recommendation system. Different from traditional item- level sequential modeling methods, we capture the interaction-level user dynamic preferences by modeling the sequential interactions. It is a high- level representation which contains auxiliary semantic information from KG. Furthermore, we adopt a joint learning manner for better representation learning by employing multi-modal fusion, which benefits from the structural constraints in KG and involves three kinds of modalities. Extensive experiments on the large-scale dataset show the better performance of our approach in making sequential recommendations in terms of both accuracy and explainability.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Yong Li
3. Chen Gao
4. Depeng Jin
5. Xing Xie
6. Fei Sun
7. Bin Cui
8. Shiwen Wu

In Information Systems

1. Xiangnan He
2. Yong Li
3. Chen Gao
4. Xing Xie
5. Depeng Jin
6. Fei Sun
7. Bin Cui
8. Shiwen Wu

Citations in the corpus

• When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “Path-based approaches, such as Hete-MF [44], SemRec [45], RuleRec [46], EIUM [47], exploit the semantic connectivity information present in the knowledge graph to regularize the user and item representations.”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “[2], [14], [44], [45], [66], [70], [81], [83], [87], [92], [93], [95], [96] MovieLens-20M [44], [86], [88], [89], [91] museums, parks, cities, etc.) to users based on their historical check-in data.”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Model Network Design Source of Explainability ECFKG [5] Meta-path based GE Meta-path over knowledge graph RippleNet [171] Meta-path based GraphSage Meta-path over knowledge graph EIUM [75] Meta-path based GE Meta-path over knowledge graph KPRN [185] Meta-path based GE Meta-path over knowledge graph ”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “There are also some attempts on explainability on GNN-based recommendation [64, 108, 157].”