Supporting top-K item exchange recommendations in large online communities
Proceedings of the 15th International Conference on Extending Database Technology
RecDB in action: recommendation made easy in relational databases
Proceedings of the VLDB Endowment
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Presently several marketplace applications over online social networks are gaining popularity. An important class of applications is online market exchange of items. Examples include peerflix.com and readitswapit.co.uk. We model this problem as a social network where each user has two associated lists. The item list consists of items the user is willing to give away to other users. The wish list consists of items the user is interested in receiving. A transaction involves a user giving an item to another user. Users are motivated to transact in expectation of realizing their wishes. Wishes may be realized by a pair of users swapping items corresponding to each other's wishes, but more generally by means of users exchanging items through a cycle, where each user gives an item to the next user in the cycle, in accordance with the receiving user's wishes.The problem we consider is how to efficiently generate recommendations for item exchange cycles, for users in a social network. Each cycle has a value which is determined by the number of items exchanged through the cycle. We focus on the problem of generating recommendations under two models. In the deterministic model, the value of a recommendation is the total number of items exchanged through cycles. In the probabilistic model, there is a probability associated with a user transacting with another user and a user being willing to trade an item for another. The value of a recommendation then is the expected number of items exchanged. We show that under both models, the problem of determining an optimal recommendation is NP-complete and develop efficient approximation algorithms for both. We show that our algorithms have guaranteed approximation factors of 2k (for greedy), 2k −1 (for local search), and(2k + 1)/3 (for combination of greedy and local search) where k is the max cycle length. We also develop a so-called maximal algorithm, which does not have an approximation guarantee but is more efficient. We conduct a comprehensive set of experiments. Our experiments show that in practice, the approximation quality achieved by maximal is competitive w.r.t. that of the other algorithms. On the other hand, maximal outperforms all other algorithms on scalability w.r.t. network size.