@inproceedings{0dc6a499eec64a1f9e106f92d2163d9c,
title = "Graph-based recommendations: Make the most out of social data",
abstract = "Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.",
keywords = "Feature extraction, Graph-based recommendations, Music recommendations, Social data",
author = "Amit Tiroshi and Shlomo Berkovsky and Kaafar, {Mohamed Ali} and David Vallet and Tsvi Kuflik",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 ; Conference date: 07-07-2014 Through 11-07-2014",
year = "2014",
doi = "https://doi.org/10.1007/978-3-319-08786-3_40",
language = "American English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "447--458",
editor = "Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben",
booktitle = "User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings",
address = "Germany",
}