Abstract
Recommender system (RS) the most successful application of Web personalization helps in alleviating the information overload available on large information spaces. It attempts to identify the most relevant items for users based on their preferences. Generally, users are allowed to provide overall ratings on experienced items but many online systems allow users to provide their ratings on different criteria. Several attempts have been made in the past to design a RS focusing on the ratings of a single criterion. However, investigation of the utility of multi criterion recommender systems in online environment is still in its infancy. We propose a multi criterion RS based on leveraging information derived from multi-criterion ratings through genetic algorithm. Experimental results are presented to demonstrate the effectiveness of the proposed recommendation strategy using a well-known Yahoo! Movies dataset.
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Parveen, R., Kant, V., Dwivedi, P., Jaiswal, A.K. (2015). Enhancing Recommendation Quality of a Multi Criterion Recommender System Using Genetic Algorithm. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_49
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DOI: https://doi.org/10.1007/978-3-319-26832-3_49
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