A Hybrid Recommendation System Based on Density-Based Clustering

Autor:	Ioannis Kompatsiaris, Stefanos Vrochidis, Ilias Gialampoukidis, Theodora Tsikrika, Anna Satsiou, Spyridon Symeonidis
Rok vydání:	2018
Předmět:	DBSCAN Computer science Collaborative filtering Recommender system computer.software_genre neighbourhood formation hybrid recommender systems MovieLens100k Leverage (statistics) Data mining Cluster analysis Density based clustering Neighbourhood (mathematics) computer clustering
Zdroj:	Internet Science ISBN: 9783319775463 INSCI Workshops
Popis:	Collaboratve filtering recommenders leverage past user-item ratings in order to predict ratings for new items. One of the most critical steps in such methods corresponds to the formation of the neighbourhood that contains the most similar users or items, so that the ratings associated with them can be employed for predicting new ratings. This work proposes to perform the combination of content-based and ratings-based evidence during the neighbourhood formation step and thus identify the most similar neighbours in a hybrid manner. To this end, DBSCAN,a density-based clustering approach, is applied for identifying the most similar users or items by considering the ratings-based and the content-based similarities, both individually and in combination. The resulting hybrid cluster-based CF recommendation scheme is then evaluated on the latest small MovieLens100k dataset and the experimental results indicate the potential of the proposed approach.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cffa45dbd15b0c6f127b3e126d86e3ce https://doi.org/10.1007/978-3-319-77547-0_5 Zobrazit plný text záznamu