Human-Centric Intelligent Systems

Volume 1, Issue 1-2, June 2021, Pages 43 - 53

Context-Based User Typicality Collaborative Filtering Recommendation

Authors
Jinzhen Zhang*, Qinghua Zhang, Zhihua Ai, Xintai Li
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
*Corresponding author. Email: 799232295@qq.com
Corresponding Author
Jinzhen Zhang
Received 7 March 2021, Accepted 24 May 2021, Available Online 20 June 2021.
DOI
https://doi.org/10.2991/hcis.k.210524.001How to use a DOI?
Keywords
Knowledge granulation; contextual information; user typicality; recommendation; granular computing
Abstract

Since contextual information significantly affecting users’ decisions, it has attracted widespread attention. User typicality indicates the preference of user for different item types, which could reflect the preference of user at a higher abstraction level than the items rated by user, and can alleviate data sparsity. But it does not consider the impact of contextual information on user typicality. This paper proposes a novel context-based user typicality collaborative filtering recommendation algorithm (named CBUTCF), which combines contextual information with user typicality to alleviate the data sparsity of context-aware collaborative filtering, and extracts, measures and integrates contextual information. First, the items are clustered and classified into different item types. For different users, the significance of contextual information for different item types is defined and measured via knowledge granulation. Then, the contextual information is combined with user typicality to measure the context-based user typicality; subsequently, the ‘neighbor’ users are determined. Finally, the unknown ratings under a single context are predicted, and the unknown ratings under multi-context are predicted according to the weighted summation of the significance of contextual information. The experimental results demonstrate that CBUTCF can effectively improve the accuracy of recommendation and increase coverage.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Human-Centric Intelligent Systems
Volume-Issue
1 - 1-2
Pages
43 - 53
Publication Date
2021/06/20
ISSN (Online)
2667-1336
DOI
https://doi.org/10.2991/hcis.k.210524.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Jinzhen Zhang
AU  - Qinghua Zhang
AU  - Zhihua Ai
AU  - Xintai Li
PY  - 2021
DA  - 2021/06/20
TI  - Context-Based User Typicality Collaborative Filtering Recommendation
JO  - Human-Centric Intelligent Systems
SP  - 43
EP  - 53
VL  - 1
IS  - 1-2
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.210524.001
DO  - https://doi.org/10.2991/hcis.k.210524.001
ID  - Zhang2021
ER  -