Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need. Just like the situation that people rely heavily on recommendation in their daily decision making processes, online users may identify desired documents more effectively and efficiently through recommendation of other users who exhibit similar interests, and/or through extracting crucial features of the users¡¦ past preferences.
Typical recommendation approaches can be classified into collaborative filtering and content-based filtering. Both approaches, however, have their own drawbacks. The purpose of this research is thus to propose a hybrid approach for text recommendations. We combine collaborative input and document content to facilitate the creation of extended content-based user profiles. These profiles are then rearranged with the technique of latent semantic indexing.
Two experiments are conducted to verify our proposed approach. The objective of these experiments is to compare the recommendation results from our proposed approach with those from the other two approaches. The results show that our approach is capable of distinguishing different degrees of document preference, and makes appropriate recommendation to users or does not make recommendation to users for uninterested documents. The application of our proposed approach is justified accordingly.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0801106-224333 |
| Date | 01 August 2006 |
| Creators | Huang, Hsin-Chieh |
| Contributors | Te-Min Chang, Wen-Feng Hsiao, Pei-Chen Sun |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0801106-224333 |
| Rights | off_campus_withheld, Copyright information available at source archive |
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