Producing timely recommendations from social networks through targeted search

There has been a significant increase in interest and participation in social networking websites recently. For many users, social networks are indispensable tools for sharing personal information and keeping abreast with updates by their acquaintances. While there has been research on understanding the structure and effects of social networks, research on using social networks for developing targeted referral systems are few even though this can be valuable because of the abundance of information about user preferences, activities and choices. The goal of this research is to develop agent-based referral systems that learn user preferences based on past rating activities and caters to an individual user’s interests by selectively searching the contributions posted by other users in close proximity in this user’s social network. In particular, we are interested in fast notification of relevant activities in the social network that will enhance user awareness, satisfaction, and currency. In this paper, we propose keeping different trust values for a friend on different topics of interest and emphasize its importance with empirical results. We have developed an online photo referral system that identifies photos of possible interest to a user based on meta-data and comments on the pages of linked users on a popular photo sharing social website (flickr.com). We develop a probabilistic category determination mechanism that allows us to identify the possible categories an item belongs to by examining its tags. We use comments as an indirect measure of user preference for a photo. Empirical results show that our Social Network-based Item Recommendation (SNIR) system outperforms a content-based approach as well as the current recommendation schemes.