Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine

Thread Recommendation and Retrieval in Online Forum Based on Manifold Alignment

Author ZhaoJun
Tutor WangQiang;WangCan
School Zhejiang University
Course Applied Computer Technology
Keywords Personalized recommender system personalized retrieval system collaborative filtering social networks manifold alignment
CLC TP391.3
Type Master's thesis
Year 2011
Downloads 61
Quotes 0
Download Dissertation

People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information overload problem. Traditional recommendation approaches can not be directly applied to online forums due to two reasons. First, unlike traditional movie or music recommendation problem, there is no rating information in online forums. Second, the sparsity problem is more severe since the users may only read threads but take no-actions. In addition, retrieval system in online forum will probably give results that not interest everybody. Thus, a personalized retrieval answer will be more preferred. To address these limitations, in this paper we propose to make use of the reply relationships among users, as well as thread contents. A learning algorithm is introduced to infer a user-thread alignment manifold in which both users and thread contents can be well represented. Thus, the relatedness between users and threads can be measured on this alignment manifold, and the closest threads which can best meet the corresponding user’s information needs are recommended. With the advance of user-thread alignment manifold, we can also capture user interests as well as user feedbacks to make personalized retrieval system. Experiments on a dataset crawled from digg.com have demonstrated the superiority of our personalized recommendation and retrieval system.

Related Dissertations
More Dissertations