Title: A new neighbourhood formation approach for solving cold-start user problem in collaborative filtering
Authors: Rahul Kumar; Pradip Kumar Bala; Shubhadeep Mukherjee
Addresses: Indian Institute of Management (IIM) Sambalpur, Jyoti Vihar, Burla, Sambalpur - 768019, Odisha, India ' Department of Information Systems, Indian Institute of Management (IIM) Ranchi, Ranchi, Jharkhand 834008, India ' Symbiosis Institute of Business Management, Hosur Road, Electronics City Phase 1, Electronic City, Bengaluru, Karnataka 560100, India
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.
Keywords: recommender systems; collaborative filtering; cold-start problem; neighbours; similarity coefficient.
International Journal of Applied Management Science, 2020 Vol.12 No.2, pp.118 - 141
Received: 12 Jun 2018
Accepted: 29 Jan 2019
Published online: 02 Apr 2020 *