@inproceedings{33af1f7c3d1e4a76a4c5fa37429d4d49,
title = "Assessing the contribution of twitters textual information to graph-based recommendation",
abstract = "Graph-based recommendation approaches can model associations between users and items alongside additional contextual information. Recent studies demonstrated that representing features extracted from social media (SM) auxiliary data, like friendships, jointly with traditional users/items ratings in the graph, contribute to recommendation accuracy. In this work, we take a step further and propose an extended graph representation that includes socio-demographic and personal traits extracted from the content posted by the user on SM. Empirical results demonstrate that processing unstructured textual information collected from Twitter and representing it in structured form in the graph improves recommendation performance, especially in cold start conditions.",
keywords = "Graph-based recommendation, Information extraction, PPR, Social media, Twitter",
author = "Pritsker, {Evgenia Wasserman} and Tsvi Kuflik and Einat Minkov",
year = "2017",
month = mar,
day = "7",
doi = "10.1145/3025171.3025218",
language = "American English",
series = "International Conference on Intelligent User Interfaces, Proceedings IUI",
publisher = "Association for Computing Machinery",
pages = "511--516",
booktitle = "IUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces",
address = "United States",
note = "22nd International Conference on Intelligent User Interfaces, IUI 2017 ; Conference date: 13-03-2017 Through 16-03-2017",
}