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Top-N Recommendation for Shared Account on Book Recommender System

Until recently, recommender systems have been widely applied. Commonly, a single account is used by only one user when interacts with a recommender system. In fact, it is possible that users share their accounts with other users, for example, a single shopping account in an online book store is used by three users in a household. Most recommender systems fail where multiple preferences are mixed in one shared account without contextual information for splitting the shared account. This paper discusses our study and implementation of COVER disambiguating item-based algorithm, a solution for shared account problems because of the absence of contextual information. It applies an item-based top-N collaborative filtering algorithm as a base algorithm. The algorithm aims to improve the item-based top-N collaborative filtering algorithm for tackling the generality, dominance, and presentation problems of the shared account. It has been tested using BookCrossing and Amazon Review datasets and it generates recommendations based on binary and positive-only feedback. To conclude, the proposed COVER disambiguating item-based algorithm has been able to reduce the score of fraction at zero recall. In addition, it can increase the identifiability score of recommendations, in comparison with the item-based top-N collaborative filtering algorithm.