A people-to-people recommendation system using Tensor Space Models
Kutty, Sangeetha, Chen, Lin, & Nayak, Richi (2012) A people-to-people recommendation system using Tensor Space Models. In Shin, DongWong (Ed.) Proceedings of the ACM Symposium on Applied Computing, ACM Press, Riva del Garda, Trento, pp. 187-192.
Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.
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|Item Type:||Conference Paper|
|Keywords:||People-to-people network, Tensor Space Model, two-way relationship, Recommender Systems, decomposition|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 The Authors|
|Deposited On:||21 Dec 2011 21:55|
|Last Modified:||07 Aug 2012 08:19|
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