Abstract
Item category has proven to be useful additional information to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a at form, in many real applications, item category is often organized in a richer knowledge structure - category hierarchy, to reect the inherent correlations among different categories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifically, a user can be inuenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the inuence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms.
Original language | English |
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Title of host publication | 32nd Annual ACM Symposium on Applied Computing, SAC 2017 |
Publisher | Association for Computing Machinery |
Pages | 1679-1684 |
Number of pages | 6 |
ISBN (Electronic) | 9781450344869 |
DOIs | |
Publication status | Published - 3 Apr 2017 |
Externally published | Yes |
Event | 32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco Duration: 4 Apr 2017 → 6 Apr 2017 |
Conference
Conference | 32nd Annual ACM Symposium on Applied Computing, SAC 2017 |
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Country/Territory | Morocco |
City | Marrakesh |
Period | 4/04/17 → 6/04/17 |
Keywords
- Category hierarchy
- Item category
- Latent factor model
- Recommender systems