@inproceedings{7c1c3c85d8eb49cbaee93642071e5db1,
title = "Opinion-based collaborative filtering to solve popularity bias in recommender systems",
abstract = "Existing recommender systems suffer from a popularity bias problem. Popular items are always recommended to users regardless whether they are related to users' preferences. In this paper, we propose an opinion-based collaborative filtering by introducing weighting functions to adjust the influence of popular items. Based on conventional user-based collaborative filtering, the weighting functions are used in measuring users' similarities so that the effect of popular items is decreased with similar opinions and increased with dissimilar ones. Experiments verify the effectiveness of our proposed approach.",
keywords = "collaborative filtering, popularity bias, recommender system",
author = "Xiangyu Zhao and Zhendong Niu and Wei Chen",
year = "2013",
doi = "10.1007/978-3-642-40173-2\_35",
language = "English",
isbn = "9783642401725",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "426--433",
booktitle = "Database and Expert Systems Applications - 24th International Conference, DEXA 2013, Proceedings",
address = "Germany",
edition = "PART 2",
note = "24th International Conference on Database and Expert Systems Applications, DEXA 2013 ; Conference date: 26-08-2013 Through 29-08-2013",
}