People recommendation based on aggregated bidirectional intentions in social network site

Authors:
Yang Sok Kim;Ashesh Mahidadia;Paul Compton;Xiongcai Cai;Mike Bain;Alfred Krzywicki;Wayne Wobcke
Affiliations:
School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia
Venue:
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Year:
2010

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Abstract

In a typical social network site, a sender initiates an interaction by sending a message to a recipient, and the recipient can decide whether or not to send a positive or negative reply. Typically a sender tries to find recipients based on his/her likings, and hopes that they will respond positively. We examined historical data from a large commercial social network site, and discovered that a baseline success rate using such a traditional approach was only 16.7%. In this paper, we propose and evaluate a new recommendation method that considers a sender's interest, along with the interest of recipients in the sender while generating recommendations. The method uses user profiles of senders and recipients, along with past data on historical interactions. The method uses a weighted harmonic mean-based aggregation function to integrate "interest of senders" and "interest of recipients in the sender". We evaluated the method on datasets from the social network site, and the results are very promising (improvement of up to 36% in success rate).