@inproceedings{d0cec350e37f48ada81034ab32589765,
title = "PathMining: A path-based user profiling algorithm for heterogeneous graph-based recommender systems",
abstract = "In a heterogeneous graph-based recommender system, relationships among various entities are used to predict the rating or preference that a user would give to an item. By modeling user's selection behavior as a path in the heterogeneous graph, we can capture each user's unique selection behaviors as a set of paths with specific types of nodes and edges. Since these paths capture selection behaviors unique to each user, these can then be used to perform personalized profiling and recommendation for each user. In this paper, we introduce PathMining, an algorithm which constructs personalized user profile and make recommendations based on Monte Carlo sampling of graph traversal. PathMining predicts preferable items by emulating user's selection processes. The performance and potential value of our method is validated by using HetRec 2011 dataset.",
author = "Sangkeun Lee and Sanghyeb Lee and Park, {Byoung Hoon}",
note = "Publisher Copyright: Copyright {\textcopyright} 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.; 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015 ; Conference date: 18-05-2015 Through 20-05-2015",
year = "2015",
language = "English",
series = "Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015",
publisher = "AAAI Press",
pages = "519--522",
editor = "William Eberle and Ingrid Russell",
booktitle = "Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015",
}