@inproceedings{3a8f31e7677e4713b338a8c667c8d96d,
title = "FISM: Factored item similarity models for Top-N recommender systems",
abstract = "The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the itemitem similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-The-Art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.",
keywords = "Item similarity, Recommender systems, Sparse data, Topn",
author = "Santosh Kabbur and Xia Ning and George Karypis",
note = "Publisher Copyright: Copyright {\textcopyright} 2013 ACM. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 ; Conference date: 11-08-2013 Through 14-08-2013",
year = "2013",
month = aug,
day = "11",
doi = "10.1145/2487575.2487589",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "659--667",
editor = "Rajesh Parekh and Jingrui He and Inderjit, {Dhillon S.} and Paul Bradley and Yehuda Koren and Rayid Ghani and Senator, {Ted E.} and Grossman, {Robert L.} and Ramasamy Uthurusamy",
booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
}