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Learning to make social recommendations: a model-based approach

Citation

Cai, X and Bain, M and Krzywicki, A and Wobcke, W and Kim, YS and Compton, P and Mahidadia, A, Learning to make social recommendations: a model-based approach, Lecture Notes in Artificial Intelligence 7121: ADMA 2011, 17-19 December 2011, Beijing, China, pp. 124-137. ISBN 978-3-642-25855-8 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 Springer

DOI: doi:10.1007/978-3-642-25856-5_10

Abstract

Social recommendation, predicting people who match other people for friendship or as potential partners in life or work, has recently become an important task in many social networking sites. Traditional content-based and collaborative filtering methods are not sufficient for people-to-people recommendation because a good match depends on the preferences of both sides. We proposed a framework for social recommendation and develop a representation for classification of interactions in online dating applications that combines content from user profiles plus interaction behaviours. We show that a standard algorithm can be used to learn a model to predict successful interactions. We also use a method to search for the best model by minimising a cost based on predicted precision and recall. To use the model in real world applications to make recommendations, we generate candidate pairs using the selected models and ranked them using a novel probabilistic ranking function to score the chance of success. Our model-based social recommender system is evaluated on historical data from a large commercial social networking site and shows improvements in success rates over both interactions with no recommendations and those with recommendations generated by standard collaborative filtering.

Item Details

Item Type:Refereed Conference Paper
Keywords:recommender systems, social network analysis, machine learning, data mining, information retrieval, social recommendation, social media
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Intelligence and Image Processing not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Software Packages (excl. Computer Games)
Author:Kim, YS (Dr Yang Kim)
ID Code:94656
Year Published:2011
Deposited By:Computing and Information Systems
Deposited On:2014-09-15
Last Modified:2014-12-09
Downloads:0

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