Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression

LIU Qingwen; XIONG Yan; HUANG Wenchao

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 712-717

LIU Qingwen, XIONG Yan, HUANG Wenchao, “Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 712-717, 2014,

Citation:

LIU Qingwen, XIONG Yan, HUANG Wenchao, “Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 712-717, 2014,

Citation:

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Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression

School of Computer Science of University of Science and Technology of China, Hefei 230026, China

Funds: This work is supported by National Natural Science Foundation of China (No.61202404, No.61170233, No.61232018, No.61272472, No.61272317, No.61300170), the Fundamental Research Funds for the Central Universities (No.WK0110000036), and University Provincial Natural Science Foundation of Anhui Province (No.KJ2013A040).

Received Date: 2013-01-01
Rev Recd Date: 2013-04-01
Publish Date: 2014-10-05

Abstract

Abstract

Collaborative filtering can be classified into user-based or item-based methods according to different assumptions. Our experiment results show that both user-based and item-based methods can reach a certain degree of accuracy, but the recommendation coverage of these two methods is significantly different. This paper qualitatively analyzes the advantages and disadvantages of such two methods, and found that user-based methods advantage in recommending popular items while item-based methods perform better at suggesting long tail items. Based on this analysis, we proposes a novel machine learning framework to systematically combine both. We train a model for each user-item pair for discovering the local preference of the user or item over both methods, and effectively combine user-based and item-based predictions using the preference information. Experiment results show that our approach can significantly improve the recommendation quality, and to a certain extend, alleviate the data sparsity problem.
- Recommender systems,
- Collaborative filtering,
- User-based,
- Item-based,
- Model combination

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References(16)

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