/ プログラム/ 発表一覧/ 著者一覧/ 企業展示一覧/ jsai2012ホーム /

3M2-IOS-3b-5 A Median Concept for Model-Based Collaborative Filtering

*セッションの無断動画配信はご遠慮下さい。

Tweet #jsai2012 このエントリーをはてなブックマークに追加

06月14日(Thu) 13:30〜18:00 M会場(-山口県自治会館/大会議室(80))
3M2-IOS-3b International Organized Session「Special Session on Web Intelligence & Data Mining (2)」

演題番号3M2-IOS-3b-5
題目A Median Concept for Model-Based Collaborative Filtering
著者Ekkawut Rojsattarat(Department of Computer Science, Kasetsart University Si Racha Campus, Thailand)
Pakaket Wattuya(Department of Computer Science, Kasetsart University)
時間06月14日(Thu) 16:00〜16:30
概要Model-based collaborative filtering has widely been used to allow the recommender systems to learn to recognize complex patterns of user preferences. So far many clustering models have been investigated to solve the shortcomings of memory-based algorithms. Mostly, the user preferences are in form of sparse matrix which comprises of many noises. Predictions of recommendations for any active users are unavoidably effected by outlier data. While the previous works rely on solving general clustering problems, we propose a general framework to apply a concept of generalized median to collaborative filtering. From a general point of view, given a distance function d(p, q), the essential information of a given set of patterns S in arbitrary space U is covered by a generalized median p ̅∈U that minimizes the sum of distances to all patterns from S. This concept has found various applications in dealing with strings, graphs, curves, and clusterings. In our current work pseudo user rating matrix which obtains by item-based collaborative filtering to control a degree of data sparsity. The various model-based collaborative filtering by generalized median concept is applied to compare overall performance in reducing the leftover effects of outliers. Additionally, we compare our approach to various collaborative filtering algorithms for performance evaluation.
論文PDFファイル