CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor杨青
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword机器学习 推荐系统
Other Abstract
Recommender System is a interlayer between information provider and users. Recommender System builds user preference models based on analyzing users' historical behaviors and contents,  providing users with content they may be interested in. User feedback in Recommender System includes explicit feedback and implicit feedback. Implicit feedback can be obtained much easier than explicit feedback, thus having a wider range of application. A Heterogeneous Information Network(HIN) is a graph with multiple types of nodes and links, and relations between nodes contain rich information. Heterogeneous Information Network can be potentially adopted to improve the recommendation quality.
Recommender System needs to interact with users. Good explanations could help inspire user's trust and loyalty, from user's perspective. In this work, we investigate explainable recommender techniques based on HIN and implicit feedback. Following are the main contributions of this paper:

ExpRec: Explicable Personalized Recommendation via Heterogeneous Information Network. We propose ExpRec model in this paper to address the problem achieving accurate TopN recommendation and explanations at the same time. Paths in HIN is explicable and a path starts from a user and ends with an item represents diffusion of user's preference. Recommend is a behavior calculating the similarity between user's preference and item feature in itself. ExpRec evaluates this similarity via a path-based score.
We propose two methods based on ExpRec model: Exp-MAX and Exp-WSUM, representing maximum score strategy and weighted sum score strategy respectively. Exp-MAX selects the highest path-based score and corresponding path as recommendation result and explanation. While Exp-WSUM models user's preference on paths, making recommendations via the weighted sum of user preferences and path-based scores on the whole path set. Experiment results show our model can produce accurate personalized recommendations and reasonable explanations at the same time.

Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
张斌. 基于异构信息网络的可解释推荐系统[D]. 北京. 中国科学院研究生院,2016.
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