Authors: Kandasamy Palanivel, Ramakrishnan Sivakumar
Addresses: Department of Computer Science, AVVM Sri Pushpam College (Autonomous), Affiliated to Bharathidasan University, Poondi, Thanjavur, Tamil Nadu, India. ' Department of Computer Science, AVVM Sri Pushpam College (Autonomous), Affiliated to Bharathidasan University, Poondi, Thanjavur, Tamil Nadu, India
Abstract: In collaborative recommender systems, the overall ratings on items do not provide more detail about the reason behind the user|s preferences. The multicriteria ratings give details about the user|s preferences in multiple aspects and provide an opportunity to compute accurate recommendations. The user ratings collected by these systems are usually subjective, imprecise and vague, because it is based on user|s perceptions and opinions. Fuzzy sets are an appropriate paradigm to handle the uncertainty and fuzziness of human behaviour. Because of these reasons, we propose a collaborative recommendation approach that uses the fuzzy linguistic approach to represent multicriteria user-item preference ratings, then finds similarities using fuzzy user-based and fuzzy item-based similarity measures and computes recommendations using fuzzy aggregation-based approach. The proposed approach|s performance is evaluated empirically against traditional user-based and item-based recommendation algorithms using a music recommender system developed for this research. From the evaluation results, it is observed that the proposed approach shows improvement in recommendations than the traditional algorithms.
Keywords: collaborative recommender systems; filtering; fuzzy linguistics; e-commerce; electronic commerce; internet; world wide web; collaboration; user preferences; multicriteria ratings; user ratings; subjectivity; imprecision; vagueness; user opinions; fuzzy sets; uncertainty; fuzziness; human behaviour; collaborative recommendations; user-item preference ratings; fuzzy items; item-based similarities; similarity measures; user-based similarities; fuzzy aggregations; recommendation algorithms; music recommender systems; user evaluation; business information systems.
International Journal of Business Information Systems, 2011 Vol.7 No.4, pp.419 - 439
Published online: 30 Sep 2014 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article