Research on the Application of Agent in Personalized Information Recommendation System

Article Preview

Abstract:

In daily life, we rely on other people's suggestions or by word of mouth or recommendation letters and book reviews printed in newspapers and general surveys, such as restaurant guides. However, the Internet's explosive growth has brought us information that anyone can be greatly difficult to digest. To cope with the flood of information, the personalized recommendation systems have been established to assist and complement the natural social process. These systems recommend users to select information that users may be interested in and filter out which users may not be interested. In order to alleviate the information overload problem, the author researches on the application of agent in personalized information recommendation system. The system collects user information of interest through the agent systems and queries the resources to filter and remove information that users do not need. It can provide users with intelligent and active information services.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 271-273)

Pages:

853-856

Citation:

Online since:

July 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] S. Maneeroj, H. Kanai, K. Hakozaki, Combining Dynamic Agents and Collaborative Filtering without Sparsity Rating Problem for Better Recommendation Quality, Proceedings of the Second DELOS Network of Excellence Workshop, 2001, pp.33-38.

Google Scholar

[2] Songjie Gong, An Efficient Collaborative Recommendation Algorithm Based on Item Clustering, Lecture notes in electrical engineering, Volume 72, pp: 381-387.

DOI: 10.1007/978-3-642-14350-2_48

Google Scholar

[3] Manos Papagelis, Dimitris Plexousakis, Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents, Engineering Application of Artificial Intelligence 18 (2005) 781-789.

DOI: 10.1016/j.engappai.2005.06.010

Google Scholar

[4] Herlocker, J. (2000). Understanding and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, Computer Science Dept., University of Minnesota.

Google Scholar

[5] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

DOI: 10.4304/jsw.5.7.745-752

Google Scholar

[6] Huang qin-hua, Ouyang wei-min, Fuzzy collaborative filtering with multiple agents, Journal of Shanghai University (English Edition), 2007, 11(3): 290-295.

DOI: 10.1007/s11741-007-0321-2

Google Scholar

[7] Songjie Gong, Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation, Journal of Software, Volume 4, Number 8, October 2009, pp: 883-890.

DOI: 10.4304/jsw.4.8.883-890

Google Scholar

[8] Foner, L N. 1997. Yenta: A Multi-Agent, Referral-Based Matchmaking System. In: Proceedings of The First International Conference on Autonomous Agents (Agents'97), 301-307. ACM Press.

DOI: 10.1145/267658.267732

Google Scholar

[9] R. Cissée. An Architecture for Agent-Based Privacy-Preserving Information Filtering. Sixth International Workshop on Trust, Privacy, Deception and Fraud in Agent Systems, (2003).

Google Scholar

[10] Foner, L N. 1996. A Security Architecture for Multi-Agent Matchmaking. In: Proceeding of The Second International Conference on Multi-Agent Systems (ICMAS'96). Keihanna Plaza, Kansai Science City, Japan.

Google Scholar