Motif Enhanced Recommendation over Heterogeneous Information Network
| Autor: | Huan Zhao, Yingqi Zhou, Yangqiu Song, Dik Lun Lee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Information retrieval Computer science RSS Computer Science - Social and Information Networks 02 engineering and technology computer.file_format Recommender system Computer Science - Information Retrieval 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Heterogeneous information Motif (music) computer Information Retrieval (cs.IR) |
| Zdroj: | CIKM |
| DOI: | 10.48550/arxiv.1908.09701 |
| Popis: | Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and items. However, existing meta-path based methods only consider first-order relations, ignoring higher-order relations among the nodes of \textit{same} type, captured by \textit{motifs}. In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations. With MEMP-based similarities between users and items, we design a recommending model MoHINRec, and experimental results on two real-world datasets, Epinions and CiaoDVD, demonstrate its superiority over existing HIN-based RS methods. Comment: CIKM 2019 camera ready version |
| Databáze: | OpenAIRE |
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