Automated Recommendation Rule Acquisition for Two-Way Interaction-based Social Network Web Sites

Abstract

A problem with social network web sites for activities such as dating or finding new friends is that often there is little positive response from those contacted. In this research we investigated historical data from a large commercial social network site to establish which subgroups of people were most likely to respond to a particular individual. Our two-way interaction model developed a table for each attribute to determine which pair of values for sender and recipient gave the best response rate. From all the attributes the user profile of a likely responder was created, but then less significant attributes were removed. With this simple technique we were able to demonstrate that where users had contacted people the system would have recommended, the success rate was 29.4% compared to a baseline success rate of 16.6%. This represents a very considerable increase in the likelihood of getting a favourable response. We are now planning a study that provides prospective recommendations to actual users, based on our model.

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Cite this paper

@inproceedings{Kim2010AutomatedRR, title={Automated Recommendation Rule Acquisition for Two-Way Interaction-based Social Network Web Sites}, author={Yang Sok Kim and Ashesh Mahidadia and Paul Compton and Alfred Krzywicki and Wayne Wobcke and Mike Bain and Xiongcai Cai}, booktitle={EKAW}, year={2010} }