Dissertation > Excellent graduate degree dissertation topics show

Research on Collaborative Filtering Algorithm Based on Items Attributes and Perference Comparison

Author: LiYouChao
Tutor: HuangGuoYan
School: Yanshan University
Course: Applied Computer Technology
Keywords: Personalized recommendation Collaborative filtering Sparsity Project Properties User clustering Preference for relatively
CLC: TP301.6
Type: Master's thesis
Year: 2010
Downloads: 140
Quote: 1
Read: Download Dissertation

Abstract


With the popularity of the Internet and the rapid development of e-commerce , network information overload has become the network users are facing a serious problem , users in the flood of information is difficult to find the necessary goods , so e-commerce recommendation system came into being. In this paper, research status at home and abroad based on the analysis of the collaborative filtering recommendation techniques are studied . First, the user rating for the sparse data case, the traditional recommendation algorithms can not guarantee the quality of recommendations , this paper presents a project-based collaborative filtering properties of user clustering algorithm, the User rating mapped to the corresponding attribute value on an item by Some attributes of common interest user clustering process , evaluation of different projects constructed similarity between users , the realization of different user groups interested in cluster analysis . Secondly, the traditional project-based scoring system is unable to find a suitable recommendation score, and a large number of intermediate aggregate score insufficient data mining , this paper presents comparative sequence preference for the project , the design of a sequence selected based on multi- domain collaborative filtering recommendation algorithm , using the selection field to find items matching sliding correlation method preference comparison value, and the characteristics of the user evaluation matrix for failure prediction and evaluation of projects . Finally, on the basis of these studies were simulated . Experimental results show that the proposed project properties and multi- domain sequence selected collaborative filtering algorithm effectively reduces the error rate recommended by the project to improve the prediction accuracy of the evaluation , reducing the rated data sparsity negative impact achieved more satisfactory recommendation quality .

Related Dissertations

  1. Establishment and Update of Similar Users’ Cluster in Personalized Information Retrieval,TP391.3
  2. Web Usage Mining and the Research of Personalized Recommendation,TP311.13
  3. Click-based user clustering research,TP311.13
  4. Remote sensing image reconstruction algorithm oriented IICCD camera is not completely random sampling,TP751
  5. Study on Collaborative Filtering Recommendation Algorithm Based on Feature Vectors,TP391.3
  6. Regional Characteristics of the Farmer-based Portable Recommendation System for Agricultural Information,S126
  7. The Application of Web Log Mining on Personalized Information Recommendaiton,TP311.13
  8. Recommended model based on Bayesian network Senate elective system applied research,G647
  9. Collaborative filtering recommendation based on social tags Strategies,TP393.09
  10. User-based Trust Model for Collaborative Recommendation Attack Defense,TP301.6
  11. User model based on hybrid collaborative filtering recommendation algorithm,TP301.6
  12. Based on data mining model of e-commerce recommendation system,F713.36
  13. Collaborative Filtering Algorithms on Netflix Dataset,TP301.6
  14. Multi-Agent Based Personalized Recommendation System,TP311.52
  15. Multi- Agent- Based Intelligent Answering System,TP311.52
  16. Two phase text classifier and classification in the recommended System,TP391.1
  17. The Research and Application of Data Mining in E-Commerce Recommendation System,TP391.3
  18. Non-ideal conditions, face recognition algorithm,TP391.41
  19. Multi-stage collaborative filtering algorithm is applied to the study of mobile commerce,TP391.3
  20. Based on fuzzy theory and its application in association rule mining personalized recommendations Application Research,TP311.13
  21. Cluster-based collaborative filtering recommendation algorithm,TP391.3

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