This thesis proposes a novel recommendation approach to take advantage of the
information available in user-created lists. Our approach assumes associations among
any two items appearing in a list together. We consider two different ways to calculate the strength of item-item associations: frequency of co-occurrence, and sum
of Bayesian ratings (SBR) of all lists containing the item pair. The latter takes into
consideration not only the number of lists the items have co-appeared in, but also
the quality of the lists. We collected a data set of user ratings for books along with
Listmania lists on Amazon.com using Amazon Web Services (AWS). Our method
shows superior performance to existing user-based and item-based collaborative
filtering approaches according to the resulted Mean Absolute Error (MAE), coverage,
precision and recall.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/2047 |
| Date | 08 January 2010 |
| Creators | Khezrzadeh, Maryam |
| Contributors | Thomo, Alex, Wadge, W. W. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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