A Content-boosted matrix factorization technique via user-item subgroups

Aslan Oğuz, Evin
This thesis mainly focuses on improving the recommendation accuracy of collaborative filtering (CF) algorithm via merging two successful approaches. Since CF algorithmsgrouplike-mindedusers,atechniquecalledMulticlassCo-Clustering(MCoC) is used in order to group like-minded users more effectively. Since, CF approaches lack incorporating content information, a content-boosted CF approach that embeds content information into recommendation process is used. In the MCoC, a user or an item can belong to zero, one or more subgroups. Thus, it is possible to predict the rating scores of users to items present in the same subgroup. However the prediction resultsfor all users anditems arenot obtainedby MCoC,since auser oran itemmay belong to zero subgroups. Therefore, content-boosted CF algorithm is applied to the whole set of users and items besides subgroups and finally the results are merged. The content-boosted approach, on the other hand, considers content information in the recommendation process. As content, the genres of movies are embedded into the item latent factor vector in the matrix factorization technique. To sum up, the content-boosted algorithm is applied to the subgroups and the whole set, and the obtained results are merged. Hence the recommendation accuracy is improved.


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Citation Formats
E. Aslan Oğuz, “A Content-boosted matrix factorization technique via user-item subgroups,” M.S. - Master of Science, Middle East Technical University, 2014.