Improving Co-Cluster Quality with Application to Product Recommendations

Michail Vlachos, Francesco Fusco, Charalambos Mavroforakis, Anastasios Kyrillidis, Vassilios G. Vassiliadis
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
Businesses store an ever increasing amount of historical customer sales data. Given the availability of such information, it is advantageous to analyze past sales, both for revealing dominant buying patterns, and for providing more targeted recommendations to clients. In this context, co-clustering has proved to be an important datamodeling primitive for revealing latent connections between two sets of entities, such as customers and products. In this work, we introduce a new algorithm for
more » ... ustering that is both scalable and highly resilient to noise. Our method is inspired by k-Means and agglomerative hierarchical clustering approaches: (i) first it searches for elementary co-clustering structures and (ii) then combines them into a better, more compact, solution. The algorithm is flexible as it does not require an explicit number of co-clusters as input, and is directly applicable on large data graphs. We apply our methodology on real sales data to analyze and visualize the connections between clients and products. We showcase a real deployment of the system, and how it has been used for driving a recommendation engine. Finally, we demonstrate that the new methodology can discover co-clusters of better quality and relevance than state-of-the-art co-clustering techniques.
doi:10.1145/2661829.2661980 dblp:conf/cikm/VlachosFMKV14 fatcat:rqsuynf5drg3namiujtw4if2eu