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PITTCULT: Recommender System using Trusted Human Network

Lee, Danielle H. and Dr, Advised and Brusilovsky, Peter (2008) PITTCULT: Recommender System using Trusted Human Network. In: Student Research Competition in Hypertext 2008.

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Official URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...

Abstract

As information on the Web continues to increase, available on more and more sites, it is increasingly important to be able to collect information in one place and filter out unnecessary information for users. PITTCULT is to share and recommend cultural event information in the Pittsburgh area. This system basically utilizes human psychology to conform to opinions of friends. When people go to a music concert or exhibition, they commonly ask their friends ' opinions, and invite them to go along. Centered on this trusted human network, users can recommend items to their friends and rate their friends ' taste about a certain genre of cultural events. As a popular recommendation technology, collaborative filtering-based recommendation (CF) is designed to find like-minded peers and is known to recommend more diverse and serendipitous recommendations. It works well in a domain where contents are not easily comparable, like music, movies, jokes and cultural events (Schafer, et al., 2007). However, CF has a data sparsity problem. If the users ' ratings are relatively fewer than the number of items, there will be too little overlap among users to make recommendation. In addition, there is cold-start problem for new users who have no or insufficient ratings (Massa & Avesani, 2007; Schafer, et al., 2007). Another problem of CF is created by ad-hoc users. Users possessing malicious intentions can harness others ' ratings to make a profit or distort the system


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Item Type: Conference or Workshop Item (Paper)
Title: PITTCULT: Recommender System using Trusted Human Network
Status: Published
Abstract: As information on the Web continues to increase, available on more and more sites, it is increasingly important to be able to collect information in one place and filter out unnecessary information for users. PITTCULT is to share and recommend cultural event information in the Pittsburgh area. This system basically utilizes human psychology to conform to opinions of friends. When people go to a music concert or exhibition, they commonly ask their friends ' opinions, and invite them to go along. Centered on this trusted human network, users can recommend items to their friends and rate their friends ' taste about a certain genre of cultural events. As a popular recommendation technology, collaborative filtering-based recommendation (CF) is designed to find like-minded peers and is known to recommend more diverse and serendipitous recommendations. It works well in a domain where contents are not easily comparable, like music, movies, jokes and cultural events (Schafer, et al., 2007). However, CF has a data sparsity problem. If the users ' ratings are relatively fewer than the number of items, there will be too little overlap among users to make recommendation. In addition, there is cold-start problem for new users who have no or insufficient ratings (Massa & Avesani, 2007; Schafer, et al., 2007). Another problem of CF is created by ad-hoc users. Users possessing malicious intentions can harness others ' ratings to make a profit or distort the system
Date: 2008
Event Title: Student Research Competition in Hypertext 2008
Event Type: Other
Refereed: Yes
Official URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...
Schools and Programs: School of Information Sciences > Information Science
Date Deposited: 25 Apr 2012 14:35
Last Modified: 25 Apr 2012 14:35

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