An Efficient Neighbourhood Estimation Technique for Making Recommendations

Abstract

Recommender systems produce personalized product recommendations during a live customer interaction, and they have achieved widespread success in e-commerce nowadays. For many recommender systems, especially the collaborative filtering based ones, neighbourhood formation is an essential algorithm component. Because in order for collaborative-filtering based recommender to make a recommendation, it is required to form a set of users sharing similar interests to the target user. Forming neighbourhood by going through all neighbours in the dataset is not desirable for large datasets containing million items and users. In this paper, we presented a novel neighbourhood estimation method which is both memory and computation efficient. Moreover, the proposed technique also leverages the common “fixed-n-neighbours” problem for standard “best-k-neighbours” techniques, therefore allows better recommendation quality for recommenders. We combined the proposed technique with a taxonomy-driven product recommender, and in our experiment, both time efficiency and recommendation quality of the recommender are improved.