Joint interaction with context operation for collaborative filtering. (April 2019)
- Record Type:
- Journal Article
- Title:
- Joint interaction with context operation for collaborative filtering. (April 2019)
- Main Title:
- Joint interaction with context operation for collaborative filtering
- Authors:
- Bai, Peizhen
Ge, Yan
Liu, Fangling
Lu, Haiping - Abstract:
- Highlights: A new model for collaborative filtering in context-aware recommendation, integrating joint interaction model with contextual operating tensor. One layer to capture user/item interactions via a tensor and another layer to capture contextual information via another tensor. Extensive experiments on four datasets and novel studies on varying contextual influence and time split recommendation. Abstract: In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between users and items. In contrast, context-aware recommender systems (CARS) use contexts to improve recommendation performance. Some early CARS models treat user, item and context equally, unable to capture contextual impact accurately. More recent models perform context operations on users and items separately, leading to "double-counting" of contextual information. This paper proposes a new model, Joint Interaction with Context Operation (JICO), to integrate the joint interaction model with the context operation model, via two layers. The joint interaction layer models interactions between users and items via an interaction tensor. The context operation layer captures contextual information via a contextual operating tensor. We evaluate JICO on four datasets and conduct novel studies, including varying contextual influence and time split recommendation. JICO consistently outperforms competing methods, while providing many useful insightsHighlights: A new model for collaborative filtering in context-aware recommendation, integrating joint interaction model with contextual operating tensor. One layer to capture user/item interactions via a tensor and another layer to capture contextual information via another tensor. Extensive experiments on four datasets and novel studies on varying contextual influence and time split recommendation. Abstract: In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between users and items. In contrast, context-aware recommender systems (CARS) use contexts to improve recommendation performance. Some early CARS models treat user, item and context equally, unable to capture contextual impact accurately. More recent models perform context operations on users and items separately, leading to "double-counting" of contextual information. This paper proposes a new model, Joint Interaction with Context Operation (JICO), to integrate the joint interaction model with the context operation model, via two layers. The joint interaction layer models interactions between users and items via an interaction tensor. The context operation layer captures contextual information via a contextual operating tensor. We evaluate JICO on four datasets and conduct novel studies, including varying contextual influence and time split recommendation. JICO consistently outperforms competing methods, while providing many useful insights to assist further analysis. … (more)
- Is Part Of:
- Pattern recognition. Volume 88(2019:Apr.)
- Journal:
- Pattern recognition
- Issue:
- Volume 88(2019:Apr.)
- Issue Display:
- Volume 88 (2019)
- Year:
- 2019
- Volume:
- 88
- Issue Sort Value:
- 2019-0088-0000-0000
- Page Start:
- 729
- Page End:
- 738
- Publication Date:
- 2019-04
- Subjects:
- Recommender system -- Collaborative filtering -- Matrix factorization -- Context aware -- Joint interaction -- Tensor
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2018.12.003 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11702.xml