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A novel context-aware recommender system based on a deep sequential learning approach (CReS).
- Source :
-
Neural Computing & Applications . Sep2021, Vol. 33 Issue 17, p11067-11090. 24p. - Publication Year :
- 2021
-
Abstract
- With an increase in online longitudinal users' interactions, capturing users' precise preferences and giving accurate recommendations have become an urgent need for all businesses. Existing sequence-aware methods generally exploit a static low-rank vector for acquiring the overall sequential features, and incorporate context information as auxiliary input. As a result, they have a restricted modeling ability for extracting multi-grained sequential behaviors over contextual information. In other words, they poorly capture the hierarchical relationship between context relations and item relations that currently influence users' preferences in a unified framework. Besides, they usually utilize users' short-term preferences with either static or irrelevant long-term representation for the prediction. To tackle the above issues, in this paper, we propose a novel Context-aware Recommender System Based on a Deep Sequential Learning Approach (CReS) to capture users' dynamic preferences by modeling the hierarchical relationships between contexts and items in a particular session, and for combining users' short-term sessions with the relevant long-term representations. Specifically, within a certain session, we design a hierarchical attention network between the identified context relations and items relations, namely CReSession. Therefore, with CReSession, we could provide a suitable session representation that mimics the hierarchical user interests on multiple granularities of contextual types and its corresponding items. We then introduce a neural attentive bi-directional GRU network to distill only those highly related to the recent short-term session. Finally, the relevant long-term representations and the short-term session are combined with the sequential residual connection to form the final user representation in a unified manner. With extensive experiments on two real-world datasets, CReS not only achieves significant improvement over the state-of-the-art methods in terms of pre-defined metrics, but also provides an interpretable result regarding why we recommend these items to users. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SEQUENTIAL learning
*DEEP learning
*RECOMMENDER systems
*DYNAMIC models
Subjects
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 33
- Issue :
- 17
- Database :
- Academic Search Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 151860795
- Full Text :
- https://doi.org/10.1007/s00521-020-05640-w