Dissertation > Excellent graduate degree dissertation topics show

Research on Collaborative Filtering by the Integration of Social Tags

Author: YaoZuo
Tutor: ZhuangYueTing;ShaoJian
School: Zhejiang University
Course: Applied Computer Technology
Keywords: Collaborative Filtering Tripartite Graph Random Walk Social Tags Lasso Logistic Regression Matrix Complete
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 182
Quote: 0
Read: Download Dissertation

Abstract


With the rapid development of Internet and the Electronic Commerce, the Recommender Systems based on Collaborative filtering (CF) technique have gained great importance from both the academia and industry. The CF approach tries to predict the utility of items for a particular user based on the items previously rated by other users. Since users always rate few items in practice, the sparsity problem is still a great challenge to researchers and bottleneck for the recommendation systems, even though the last two decades witnessed a lot of good algorithms and models trying to alleviate that problem.The social tags, popular along with the advance of web2.0, is a new cue for the recommendation systems. On one hand, it may reflect users’preference; on the other hand, it may describe items’semantic content. In this thesis, we focus on how to make full use of the social tags to improve the recommender systems.More specifically, the tripartite graph model is used to represent the users, items, social tags and their relationships and the random walk algorithm is applied to rank the items for a particular user. We build this tripartite graph in two different ways, that is, the user-centered approach and item-centered approach by trying to explore the user-tag and the item-tag relationships respectively, to try to discuss the utility of the social tags and the experiment results show the effectiveness of our proposed algorithms and the pros and cons of them.Besides, in order to alleviate the negative effects of social tags, such like noisy and sparsity, the Lasso logistic regression models is conducted to find similar tags in their semantic space. Thus we could expend the relationship between items and tags, which means that the items could be described more accurately, by annotating items using these similar tags.At last, we review the current work on Matrix Complete, and some prior experiments have been conducted to see how to apply this model to collaborative filtering area in the future.

Related Dissertations

  1. C2C e-commerce mode based product recommendation system applied research,F724.6
  2. A Study on Collaborative Recommendation Model Based on Rough Set,TP18
  3. Key technology research initiative recommendation system based on collaborative filtering,TP311.52
  4. Based on Artificial Immune Algorithm Research commerce recommendation system,TP181
  5. Research of Adult College Specialties Recommendation System Based on Data Mining and Collaborative Filtering,TP391.3
  6. Breast cancer microarray experimental data analysis and mining,R737.9
  7. Research of Sparse Problem in Collaborative Filtering Based on Tag,F713.36
  8. Design and Implementation of the College Entrance Examination Information Recommender System,TP391.3
  9. Sparse data-oriented collaborative filtering recommendation algorithm,TP301.6
  10. Collaborative filtering technology resources in personalized recommendation of Applied Research,TP391.3
  11. The Research of Personalized Recommender Algorithms Based on Correlation and Associated Properties Preference,TP391.3
  12. Research on Collaborative Filtering Algorithm in E-Commence Recommender System,TP391.41
  13. E-commerce recommendation system user clustering problem with change of user interest,F224
  14. Trust-based collaborative filtering recommendation model,F224
  15. Research on Collaborative Filtering Recommendation Method Based on RFM Model and Clustering,F713.36
  16. Design and Research of E-Commerce Ecommendation System Based on Users Behavior,TP311.52
  17. Based on Social Network Research Collaborative Filtering Recommendation,TP393.09
  18. User-based Trust Model for Collaborative Recommendation Attack Defense,TP301.6
  19. User model based on hybrid collaborative filtering recommendation algorithm,TP301.6
  20. Research and Application of Recommendation Technologies Based on Agent,TP18

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
© 2012 www.DissertationTopic.Net  Mobile