An Effective Recommendation Model using User Access Sequence and Context Entropy

An Effective Recommendation Model using User Access Sequence and Context Entropy

Xiaoyi Deng Feifei Huangfu 

College of Business Administration, Huaqiao University P.R. China, No.269, Chenghua North Rd., Quanzhou 362021

College of Foreign Languages, Huaqiao University P.R. China, No.269, Chenghua North Rd., Quanzhou 362021

Corresponding Author Email: 
londonbell@hqu.edu.cn; huangfu@hqu.edu.cn
Page: 
57-73
|
DOI: 
https://doi.org/10.18280/ama_b.600104
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

Collaborative filtering (CF) is one of the most successful recommendation technologies to cope with information overload problem. Conventional neighborhood-based CF models solely use user/item similarities instead of existing user preferences to form neighborhoods, which prediction accuracy excessively relies on. Besides, customers’ interests and demands may vary with contexts in different environment. As a result, the recommendations quality of conventional CF models would suffer. To address these issues, this paper developed an effective hybrid CF model by integrating user access sequence and context entropy. The user access sequence is introduced to mitigate the new user cold-start problem, and the context entropy is introduced for measuring uncertainty degree of user rating under different context and calculating user similarity for gathering the most similar users. Experiments on real-world datasets are carried out to compare our method’s accuracy with other three algorithms. The results show our method outperforms other methods and improves recommendation quality effectively.

Keywords: 

collaborative filtering, user access sequence, context entropy, cold-start, nearest neighbor selection, recommender system

1. Introduction
2. Background and Related Work
3. Hybrid Model based on User Access Sequence and Context Entropy
4. Experiments and Results
5. Conclusion
Acknowledgment

This work is supported by the National Natural Science Foundation of China (No.71401058), the Program for New Century Excellent Talents in Fujian Province University, NCETFJ (No.Z1625110), and the Project of Science and Technology Plan of Fujian Province of China (No.2017H01010065)

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