A hybrid collaborative filtering model with context and folksonomy for social recommendation

A hybrid collaborative filtering model with context and folksonomy for social recommendation

Xiaoyi Deng Cheng Wang 

Business School, Huaqiao University, Quanzhou 362021, China

Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen 361021, China

Corresponding Author Email: 
londonbell.deng@gmail.com
Page: 
139-157
|
DOI: 
https://doi.org/10.3166/ ISI.23.5.139-157
Received: 
|
Accepted: 
|
Published: 
31 October 2018
| Citation

OPEN ACCESS

Abstract: 

To address data sparsity problem and lack of context in neighbourhood-based collaborative filtering (CF), this paper proposes a hybrid CF model combining context and tag information. Firstly, all users were divided into different groups by their profile and contextual information using clustering, aiming to reduce the sparsity and dimension of ratings data. Then, a folksonomy network model (FNM) was developed based on tag information to analyze the relevance between different items. Then, the FNM was incorporated into the similarity measuring process of neighbourhood-based CF for the improvement of recommendation accuracy. Through the experiments on three real-world datasets, it is clear that our method outperforms other methods in recommendation quality, which means our model is more applicable in situations where context and folksonomy are critical to the success of the application, just like in social commerce and virtual community websites.

Keywords: 

collaborative filtering, hybrid recommendation, context, folksonomy, social tag

1. Introduction
2. Related work
3. Proposed model
4. Experimental results
5. Conclusion
Acknowledgment

The authors gratefully acknowledge the supports from the National Natural Science Foundation of China (No.71401058), the Project of Science and Technology Plan of Fujian Province of China (No.2017H01010065), and the Program for New Century Excellent Talents in Fujian Province University, NCETFJ (No.Z1625110).

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