臺灣博碩士論文加值系統

Recommender systems have become dominant to reduce information overload and customize information access. The most successful recommender systems is collaborative filtering, which considers preferences of other users sharing similar interests. A major problem of collaborative filtering is the sparsity problem, which refers to a situation in which transactional data is sparse and insufficient to identify similarities in user interests. Accordingly, this research proposed two second-degree neighborhood methods to alleviate the sparsity problem in collaborative filtering. Extensive experiments using EachMovie data is used to analyze the characteristics of our methods. The results show that our approach contributes to the improvement of prediction quality of recommendations, especially the sparsity problem.

Recommender systems have become dominant to reduce information overload and customize information access. The most successful recommender systems is collaborative filtering, which considers preferences of other users sharing similar interests. A major problem of collaborative filtering is the sparsity problem, which refers to a situation in which transactional data is sparse and insufficient to identify similarities in user interests. Accordingly, this research proposed two second-degree neighborhood methods to alleviate the sparsity problem in collaborative filtering. Extensive experiments using EachMovie data is used to analyze the characteristics of our methods. The results show that our approach contributes to the improvement of prediction quality of recommendations, especially the sparsity problem.

Contents

1 Introduction
1.1 Background and Motivation ..………………………………………… 1
1.2 Objectives ..………...…………………………………………………. 2
1.3 Research Procedure …………………………………………………... 3
2 Recommender Systems and Related Works
2.1 Recommender Systems ……..………………………………………... 4
2.2 Taxonomies of Recommender Systems …………………………… 5
2.2.1 Content-Based Filtering ………..………….………………….. 6
2.2.2 Collaborative Filtering ………………………………………... 6
2.3 Approaches to Collaborative Filtering ………………………………... 10
2.4 User Correlation ……………………………………………………… 10
2.5 Neighborhood-Based Algorithm ……………………………………... 12
2.6 Top-N Recommendation ……………………………………………… 13
3 Methodology
3.1 Basic Concept …………. …………………………………………….. 14
3.1.1 Relationship of user to user .….................................................. 15
3.1.2 Relationship of user to neighborhood ..……………………….. 16
3.1.2.1 First-degree neighborhood ..…………………………….. 16
3.1.2.2 Second-degree neighborhood ….………………………... 17
3.1.3 Active user …………………...….…………………………….. 18
3.2 Traditional Collaborative Filtering …………………………………… 19
3.3 Hybrid method ……………….…....………………………………….. 20
3.4 CF enhanced with SDR ….…………………………………………… 25
3.4.1 First-degree recommendation ……………...………………….. 25
3.4.2 Second-degree recommender …………………………………. 26
3.4.3 Recommendation to active user …………………....…………. 26
3.4.4 Example of CF enhanced with SDR ……………..…………… 27
3.5 CF enhanced with SDR and user profile ...…………………………… 29
3.5.1 Second-degree recommendation with active user’s profile …... 29
3.5.2 Example of CF enhanced with SDR and user profile ………… 30

4 Experimental Results
4.1 Experimental Data …………………………………………………… 32
4.2 Experimental Platform and Database Schema ……………………….. 32
4.3 Experimental Setup …………………………………………………... 34
4.4 Evaluation Metrics ……………………………………………………. 35
4.5 Evaluation Results ……………………………………………………. 37
4.5.1 Collaborative filtering with FDR …………. ………………….. 37
4.5.2 Hybrid method ………..…………………….………..………... 39
4.5.3 CF enhanced with SDR ……………………………………….. 41
4.5.4 CF enhanced with SDR and user profile ……………………… 43
4.5.5 Compare our proposed methods with other methods …………. 45
4.5.5.1 Compare with collaborative filtering/FDR ……………... 45
4.5.5.2 Compare with hybrid method …………………………… 46
5 Conclusions and Future Work 47
References 48
Appendix A Table Schema for Experiments 51

References


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