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Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling

Abstract:

With the rapid growth of social tagging systems, many efforts have been put on tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping both the tag-based user and item profiles to an abstract deep feature space, where the deepsemantic similarities between users and their target items (resp., irrelevant items) are maximize...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/2983323.2983874

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/J008346/1
EP/L012138/1
EP/M025268/1
Publisher:
Association for Computing Machinery
Host title:
CIKM 2016: 25th ACM International Conference on Information and Knowledge Management
Journal:
25th ACM International Conference on Information and Knowledge Management More from this journal
Publication date:
2016-01-01
Acceptance date:
2016-07-19
DOI:
Keywords:
Pubs id:
pubs:641625
UUID:
uuid:f64f71ec-a0f3-4c0a-a793-f55e0215ddb3
Local pid:
pubs:641625
Source identifiers:
641625
Deposit date:
2016-09-06

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