A Cluster-indexing CBR Model for Collaborative Filtering Recommendation
Tae Hyup Roh, Kyong Joo Oh, Ingoo Han · Journal of the Association for Information Systems · 2003
Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring and a final prediction step, while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (SelfOrganizing Map) and CBR (Case Based Reasoning), by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR, with validation against control algorithms through an open dataset of user preference.