2018/10/01 by Zahra Vahidi Ferdousi, Ferdousi, Zahra Vahidi, Dario Colazzo +3
Computer Science · #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1810.00751
openalex publication_date 2018/10/01 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Recommender systems help users to find their appropriate items among large\nvolumes of information. Different types of recommender systems have been\nproposed. Among these, context-aware recommender systems aim at personalizing\nas much as possible the recommendations based on the context situation in which\nthe user is. In this paper we present an approach integrating contextual\ninformation into the recommendation process by modeling either item-based or\nuser-based influence of the context on ratings, using the Pearson Correlation\nCoefficient. The proposed solution aims at taking advantage of content and\ncontextual information in the recommendation process. We evaluate and show\neffectiveness of our approach on three different contextual datasets and\nanalyze the performances of the variants of our approach based on the\ncharacteristics of these datasets, especially the sparsity level of the input\ndata and amount of available information.\n