Harnessing the power of "favorites" lists for recommendation systems
| dc.contributor.author | Khezrzadeh, Maryam | |
| dc.contributor.supervisor | Thomo, Alex | |
| dc.contributor.supervisor | Wadge, W. W. | |
| dc.date.accessioned | 2010-01-08T16:12:34Z | |
| dc.date.available | 2010-01-08T16:12:34Z | |
| dc.date.copyright | 2009 | en |
| dc.date.issued | 2010-01-08T16:12:34Z | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Master of Science M.Sc. | en |
| dc.description.abstract | 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. | en |
| dc.identifier.uri | http://hdl.handle.net/1828/2047 | |
| dc.language | English | eng |
| dc.language.iso | en | en |
| dc.rights | Available to the World Wide Web | en |
| dc.subject | Recommendation system | en |
| dc.subject | Association analysis | en |
| dc.subject | Amazon | en |
| dc.subject | Frequency | en |
| dc.subject | Bayesian rating | en |
| dc.subject | Collaborative filtering | en |
| dc.subject | CIRC | en |
| dc.subject.lcsh | UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science | en |
| dc.title | Harnessing the power of "favorites" lists for recommendation systems | en |
| dc.type | Thesis | en |