Ji yu tu chuan bo de tui jian xi tong

Wang, Dingyan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 97-110).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Motivations --- p.6Chapter 1.3 --- Contributions --- p.9Chapter 1.4 --- Organizations of This Thesis --- p.11Chapter 2 --- Background --- p.14Chapter 2.1 --- Label Propagation Learning Framework --- p.14Chapter 2.1.1 --- Graph-based Semi-supervised Learning --- p.14Chapter 2.1.2 --- Green's Function Learning Framework --- p.16Chapter 2.2 --- Recommendation Methods --- p.19Chapter 2.2.1 --- Traditional Memory-based Methods --- p.19Chapter 2.2.2 --- Traditional Model-based Methods --- p.20Chapter 2.2.3 --- Label Propagation Recommendation Models --- p.22Chapter 2.2.4 --- Latent Feature Recommendation Models . --- p.24Chapter 2.2.5 --- Social Recommendation Models --- p.25Chapter 2.2.6 --- Tag-based Recommendation Models --- p.25Chapter 3 --- Recommendation with Latent Features --- p.28Chapter 3.1 --- Motivation and Contributions --- p.28Chapter 3.2 --- Item Graph --- p.30Chapter 3.2.1 --- Item Graph Definition --- p.30Chapter 3.2.2 --- Item Graph Construction --- p.31Chapter 3.3 --- Label Propagation Recommendation Model with Latent Features --- p.33Chapter 3.3.1 --- Latent Feature Analysis --- p.33Chapter 3.3.2 --- Probabilistic Matrix Factorization --- p.35Chapter 3.3.3 --- Similarity Consistency Between Global and Local Views (SCGL) --- p.39Chapter 3.3.4 --- Item-based Green's Function Recommendation Based on SCGL --- p.41Chapter 3.4 --- Experiments --- p.41Chapter 3.4.1 --- Dataset --- p.43Chapter 3.4.2 --- Baseline Methods --- p.43Chapter 3.4.3 --- Metrics --- p.45Chapter 3.4.4 --- Experimental Procedure --- p.45Chapter 3.4.5 --- Impact of Weight Parameter u --- p.46Chapter 3.4.6 --- Performance Comparison --- p.48Chapter 3.5 --- Summary --- p.50Chapter 4 --- Recommendation with Social Network --- p.51Chapter 4.1 --- Limitation and Contributions --- p.51Chapter 4.2 --- A Social Recommendation Framework --- p.55Chapter 4.2.1 --- Social Network --- p.55Chapter 4.2.2 --- User Graph --- p.57Chapter 4.2.3 --- Social-User Graph --- p.59Chapter 4.3 --- Experimental Analysis --- p.60Chapter 4.3.1 --- Dataset --- p.61Chapter 4.3.2 --- Metrics --- p.63Chapter 4.3.3 --- Experiment Setting --- p.64Chapter 4.3.4 --- Impact of Control Parameter u --- p.65Chapter 4.3.5 --- Performance Comparison --- p.67Chapter 4.4 --- Summary --- p.69Chapter 5 --- Recommendation with Tags --- p.71Chapter 5.1 --- Limitation and Contributions --- p.71Chapter 5.2 --- Tag-Based User Modeling --- p.75Chapter 5.2.1 --- Tag Preference --- p.75Chapter 5.2.2 --- Tag Relevance --- p.78Chapter 5.2.3 --- User Interest Similarity --- p.80Chapter 5.3 --- Tag-Based Label Propagation Recommendation --- p.83Chapter 5.4 --- Experimental Analysis --- p.84Chapter 5.4.1 --- Douban Dataset --- p.85Chapter 5.4.2 --- Experiment Setting --- p.86Chapter 5.4.3 --- Metrics --- p.87Chapter 5.4.4 --- Impact of Tag and Rating --- p.88Chapter 5.4.5 --- Performance Comparison --- p.90Chapter 5.5 --- Summary --- p.92Chapter 6 --- Conclusions and Future Work --- p.94Chapter 6.0.1 --- Conclusions --- p.94Chapter 6.0.2 --- Future Work --- p.96Bibliography --- p.9