Dissertation
Incorporating domain information with collaborative filtering
Doctor of Philosophy (Ph.D.), Drexel University
May 2020
DOI:
https://doi.org/10.17918/00000235
Abstract
With the ever-growing volume of online information, recommender systems have become an effective strategy to reduce information overload. Collaborative Filtering (CF) is a widely used technique by modern recommender systems. CF uses the observed preferences/behaviors of a group of users to make predictions on the un-known preferences/behaviors of other users. Direct rely on user behaviors allows CF to uncover complex and unexpected patterns without understanding item content. While generally more accurate than other recommendation approaches, such as Content-based recommendation, Collaborative Filtering suffers from data sparsity and cold start problems due to its lack of capability to deal with limited user-item interaction data.
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Details
- Title
- Incorporating domain information with collaborative filtering
- Creators
- Yizhou Zang
- Contributors
- Xiaohua Hu (Advisor)Weimao Ke (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xii, 87 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Information Science (Informatics) (2013-2026); College of Computing and Informatics (2013-2026); Drexel University
- Other Identifier
- 991014695142104721