@inproceedings{a51f4e2360684ccb9bc98ddc8c89a853,
title = "A Random Walk Based Model Incorporating Social Information for Recommendations",
abstract = "Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sparsity and cold start problems in recommendation systems. More precisely, we construct a directed graph whose nodes consist of items and users, together with item content, user profile and social network information. We incorporate user's ratings into edge settings in the graph model. The model provides personalized recommendations and predictions to individuals and groups. The proposed algorithms are evaluated on MovieLens and Epinions datasets. Experimental results show that the proposed methods perform well compared with other graph-based methods, especially in the cold start case.",
keywords = "Recommendation system, hybrid collaborative filtering model, random walk, social networks",
author = "Shang Shang and Kulkarni, {Sanjeev R.} and Cuff, {Paul W.} and Pan Hui",
year = "2012",
doi = "10.1109/MLSP.2012.6349732",
language = "English (US)",
isbn = "9781467310260",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
booktitle = "2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012",
address = "United States",
note = "2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 ; Conference date: 23-09-2012 Through 26-09-2012",
}