Targeted Dot Product Representation for Friend Recommendation in Online Social Networks
Authors: 
Minh D. Dao, Akshay Rangamani, Sang Peter Chin, Nam P. Nguyen, Trac D. TranAuthors Info & Claims
Minh D. Dao, Akshay Rangamani, Sang Peter Chin, Nam P. Nguyen, Trac D. TranAuthors Info & ClaimsPages 349 - 356
Abstract
In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.
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- Targeted Dot Product Representation for Friend Recommendation in Online Social Networks
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Published: 25 August 2015
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