Combining Collaborative Filtering and Clustering for Implicit Recommender System
Simon Renaud-Deputter, Tengke Xiong, Shengrui Wang · 2013
Recommender systems are becoming a widespread technology used to promote cross-selling. Collaborative filtering is one of the main paradigms employed to offer recommendations to users. However, while most collaborative filtering methods require explicit user feedback, such as ratings, it is a well-established fact that users rate only a small portion of all available products. Subsequently, the rating system often acquires insufficient explicit feedback, thus leading to unsatisfactory recommendations. We propose a novel approach in the implicit feedback recommender system domain that combines clustering and matrix factorization to yield good results while using only implicit feedback on users purchase history and without requiring any parameter. We use a high-dimensional, parameter-free, divisive hierarchical clustering technique and, based on the clustering results, create personalized recommendations of high interest for each user. This easy to implement and very effective technique can be applied to any data sets where we can identify users with a purchase history.