Dissertation > Excellent graduate degree dissertation topics show

Research on Personalized Recommendation Algorithm Based on Natural Forgetting

Author: SunHaiChao
Tutor: ZhouYanJun
School: Northeast Normal University
Course: Computer Software and Theory
Keywords: Personalized Service Recommender Systems Collaborative Filtering Forgetting Curves Forgetting Function
CLC: TP311.52
Type: Master's thesis
Year: 2011
Downloads: 23
Quote: 0
Read: Download Dissertation

Abstract


With the rapid development of Internet and information technology, various types of data in the web grow quickly. The expansion of information resources causes the emergence of the so-called“information overload”and“information maze”phenomena. These kinds of phenomena make people difficult to find valuable information among the huge amount of information resources. Thus in order to make people more accurately and effectively to obtain useful information, recommender systems that could initiatively provide information for users come into being. Recommender system is based on users(?) browse histories, the preferences to items, or the relevance between items to build users(?) interest model. And then according to this model it could recommend items that may be interesting to users. For example, a movie web could base on the user(?)s view histories and the scores to films, and obtain the preferences information on films, and as a result recommend films to this user that may be interested in.Researchers have found that personalized recommendation issue exist the phenomenon of user interest drifting. That(?)s to say, people(?)s interests are not static, but will vary over time, while the traditional recommender methods do not take this into account. Therefore, based on the traditional collaborative filtering and memory and forgetting theory, we propose an algorithm that is based on natural forgetting to track the change rules of users(?) interests. The improved algorithm not only could reflect the change of interests over time, but also could recommend items according to the amount of forgotten.The main idea of the proposed users(?) drifting interest algorithm is to find appropriate time weight for items, namely, the ratings recently given by users could provide more impact on the current item. Intuitively, we think that the recent ratings are the user(?)s current main interest points; the recent ratings have a bigger time weight than others. The main work of this paper includes the following aspects: first, the related technologies of collaborative filtering are described; second, we introduce the evaluation metrics of recommender system; next, we review the modeling methods of users(?) drifting interests; finally, based on natural forgetting, a personalized recommender algorithm is proposed. This paper considering users(?) past ratings adopts a kind of time decay function that simulate natural forgetting law, track the change rule of users(?) interests, and then recommend items that is most likely to be interesting to users. Experimental results show that the proposed personalized recommender method, based on natural forgetting, could more accurately recommend items to users, and therefore improve the performance of the recommender systems.

Related Dissertations

  1. Establishment and Update of Similar Users’ Cluster in Personalized Information Retrieval,TP391.3
  2. Social tagging systems based personalized recommendation engine technology,TP391.3
  3. Personalized role-based task push mechanism of resource services,TP311.52
  4. Research on Personalization Recommender Technologies Based on User’s Interesting and Behavior of Browsing,TP391.3
  5. Study on Collaborative Filtering Recommendation Algorithm Based on Feature Vectors,TP391.3
  6. Research and Application of Resource Recommendation Based on Pragmatics Context,TP391.3
  7. Agent e-commerce recommendation system collaborative filtering technology,F713.36
  8. Research on Dual Information Source Model-Based Collaborative Filtering Algorithms,TP301.6
  9. Research on Personalized Recommender Algorithm in E-Commerce Based on Collaborative Filtering,F713.36
  10. Research of Recommendation Algorithms Based on Collaborative Filtering,TP301.6
  11. Study on Collaborative Filtering Recommendation Based on Users’ Interest Clustering,TP393.09
  12. The Film Website Construction Based on a Collaborative Filtering Recommendation Algorithm,TP393.092
  13. Research on Key Technologies of E-commerce Personalized Recommendation,TP301.6
  14. Research and Implementation for Collaborative Filtering Optimization Algorithm,TP301.6
  15. Collaborative Filtering Method Using in Recommender System of E-commerce,TP391.3
  16. Research on Collaborative Recommendation System Based on User Clustering,TP391.3
  17. The Research of Hybrid Collaborative Filtering Technologies Based on User-Item,TP391.3
  18. Collaborative Filtering Recommendation Algorithm Based on Cloud Model and It’s Application,TP301.6
  19. Application Reseach of Data Mining on E-Commerce Recommendation System,TP311.13
  20. The Research on Personalized Recommendation Technology in Network-Teaching,TP391.3

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Development
© 2012 www.DissertationTopic.Net  Mobile