A new neighbourhood formation approach for solving cold-start user problem in collaborative filtering

Abstract

Collaborative filtering (CF) is the most widely accepted recommendation technique. Despite its popularity, this approach faces some major challenges like that of a cold-start user problem where a user has rated a handful of items. Due to very few ratings available for the cold-start users, their similarities with rest of the users has been questioned in the past, none have focused on their approach for neighbour identification. Whilst the traditional CF approaches select only those similar users as neighbours who have rated the item under consideration, the neighbourhood comprises of weak neighbours of the cold-start users. To address this shortcoming, our proposed approach selects neighbours with highest similarity irrespective of their availability of ratings for that item. Moreover, for the selected similar neighbours with missing ratings, an item based regression is performed to partially populate the matrix. The efficacy of the proposed neighbourhood formation approach addressing cold-start user problem is validated on two publicly available MovieLens datasets. Our approach provides superior quality of recommendations evaluated on a range of prediction and classification accuracy metrics. The results are encouraging particularly for systems having higher percentage of cold-start users which indicates the effectiveness of our approach in practical settings of new internet portals.