@inproceedings{6bfb4b9c62544c34a14eec6270e8e5ba,
title = "CBPF: Leveraging context and content information for better recommendations",
abstract = "Recommender systems (RS) help users to find their appropriate items among large volumes of information. Among the different types of RS, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.",
keywords = "Context-aware recommender system, Contextual information integration, Pre-filtering recommender system",
author = "\{Vahidi Ferdousi\}, Zahra and Dario Colazzo and Elsa Negre",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 14th International Conference on Advanced Data Mining and Applications, ADMA 2018 ; Conference date: 16-11-2018 Through 18-11-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-05090-0\_32",
language = "English",
isbn = "9783030050894",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "381--391",
editor = "Guojun Gan and Xue Li and Shuliang Wang and Bohan Li",
booktitle = "Advanced Data Mining and Applications - 14th International Conference, ADMA 2018, Proceedings",
}