A Monte Carlo algorithm for cold start recommendation
Refereed conference paper presented and published in conference proceedings


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AbstractRecommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings on items. Although many models and algorithms have been proposed, how to make accurate prediction for new users with extremely few rating records still remains a big challenge, which is called the cold start problem. Many existing methods utilize additional information, such as social graphs, to cope with the cold start problem. However, the side information may not always be available. In contrast to such methods, we propose a more general solution to address the cold start problem based on the observed user rating records only. Specifically we define a random walk on a bipartite graph of users and items to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold start users. Then we propose a Monte Carlo algorithm to estimate the similarity between different users. This algorithm takes a precomputation approach, and thus can efficiently compute the user similarity given any new user for rating prediction. In addition, our algorithm can easily handle dynamic updates and can be parallelized naturally, which are crucial for large recommendation systems. Theoretical analysis is presented to demonstrate the efficiency and effectiveness of our algorithm, and extensive experiments also confirm our theoretical findings. Copyright is held by the International World Wide Web Conference Committee (IW3C2).
All Author(s) ListRong Y., Wen X., Cheng H.
Name of Conference23rd International Conference on World Wide Web, WWW 2014
Start Date of Conference07/04/2014
End Date of Conference11/04/2014
Place of ConferenceSeoul
Country/Region of ConferenceSouth Korea
Year2014
Month1
Day1
Pages327 - 336
ISBN9781450327442
LanguagesEnglish-United Kingdom
KeywordsCold start, Monte Carlo simulation, Preference propagation, Random walk on bipartite graph

Last updated on 2021-16-01 at 01:05