Recommender systems are being widely applied in many E-commerce sites to suggest products, services, and information items to potential users. Collabora-tive filtering systems, the most successful recommender system technology to date, help people make choices based on the opinions of other people. While collaborative filtering systems have been a substantial success, there are sev-eral problems that researchers and commercial applications have identified: the early rater problem, the sparsity problem, and the large scale problem. Moreover, existing collaborative filtering systems do not consider data re-cency. For this reason, if a user¡¯s preferences have changed over time, the sys-tems might not recognize it quickly. This thesis studies how to apply data re-cency to collaborative filtering systems to get more predictive accuracy. We define the data recency problem as the negative impact of old data on the pre-dictive accuracy of collaborative filtering systems. In order to mitigate this shortcoming, the combinations of time-based forgetting mechanisms, pruning and non-pruning strategies and linear and kernel functions, are utilized to ap-ply weights. A clustering technique is employed to detect the user¡¯s changing preferences. We apply our research approach to the DeliBook dataset. The goal of our experiments is to show that our algorithm that incorporates tempo-ral factors provides better recommendations than existing methods.

Creator

• Kim, Yoonsoo

Contributors

• Brown, David C.

Degree

• MS

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-09244-024515

Keyword

• Data recency problem
• Recommender system
• Time-based forgetting function
• Time-based forgetting strategy
• Collaborative filtering system

Advisor

• Brown, David C.

Defense date

• 2004-09-23

Year

• 2004

Date created

• 2004-09-24

Resource type

• Thesis

Rights statement

• In Copyright