Scalable And Interpretable Product Recommendations Via Overlapping Co-Clustering
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017)(2017)
摘要
We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).
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关键词
interpretable product recommendations,overlapping coclustering,positive feedback,implicit feedback,very large datasets,linear complexity,industrial data,publicly available datasets,recommendation accuracy,matrix factorization,textually interpretable recommendation,visually interpretable recommendation,graphical processing units,GPU,scalable product recommendations
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