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Conference Papers Year : 2020

Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

Abstract

This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary “next step”. Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights.
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hal-04050584 , version 1 (29-03-2023)

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Dimosthenis Beleveslis, Christos Tjortjis. Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.452-461, ⟨10.1007/978-3-030-49161-1_38⟩. ⟨hal-04050584⟩
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