Friend Recommendation Using Probabilistic Matrix Co-Factorization
- 발행기관 Pohang University of Science and Technnology
- 지도교수 Choi Seungjin
- 발행년도 2012
- 학위수여년월 2012. 8
- 학위명 석사
- 학과 및 전공 일반대학원 정보전자융합공학부
- 원문페이지 40
- 실제URI http://www.dcollection.net/handler/postech/000001390750
- 본문언어 영어
초록/요약
Nowadays, the number of users on social network sites or e-commercial sites becomes much larger and users’ information on those sites are normally heterogeneous, so friend recommendation becomes more and more an important issue. Many researches have been proposed such as the graph-based approach or content-based approach or hybrid ones, however the model-based method is few, especially the one can utilize a variety of user information. Advancing previous work, in this thesis we present a novel model-based algorithm which can incorporate both the friendship graph and the user rating matrix to learn sensible user descriptors for making friend recommendations. Then, for the experiments we use the benchmark Filmtipset dataset and prove that our algorithm outperforms the base-line methods.
more목차
1 Introduction 1
2 Related Work 4
3 Algorithm 7
3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 FriendPMCF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2.1 Probabilistic Matrix Co-Factorization (PMCF) . . . . . . . 8
3.2.2 Friend Recommendation . . . . . . . . . . . . . . . . . . . . 11
4 Experiment 13
4.1 Description of the Filmtipset Dataset . . . . . . . . . . . . . . . . 13
4.2 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Comparison of Performance . . . . . . . . . . . . . . . . . . . . . . 16
5 Conclusion 21
한글 요약문 23
Bibliography 24