Supporting top-K item exchange recommendations in large online communities

Abstract

Item exchange is becoming popular in many online community systems, e.g. online games and social network web sites. Traditional manual search for possible exchange pairs is neither efficient nor effective. Automatic exchange pairing is increasingly important in such community systems, and can potentially lead to new business opportunities. To facilitate item exchange, each user in the system is entitled to list some items he/she no longer needs, as well as some required items he/she is seeking for. Given the values of all items, an exchange between two users is eligible if 1) they both have some unneeded items the other one wants, and 2) the exchange items from both sides are approximately of the same total value. To efficiently support exchange recommendation with frequent updates on the listed items, new data structures are proposed in this thesis to maintain promising exchange pairs for each user. Extensive experiments on both synthetic and real data sets are conducted to evaluate our proposed solutions.