Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Anonymity-proof Shapley value: extending shapley value for coalitional games in open environments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Pricing Strategies for Viral Marketing on Social Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Proceedings of the fifth ACM international conference on Web search and data mining
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Suppose you buy a new laptop and, simply because you like it so much, you recommend it to friends, encouraging them to purchase it as well. What would be an adequate price for the vendor of the laptop to pay for your recommendation? Personal recommendations like this are of considerable commercial interest, but unlike in sponsored search auctions there can be no truthful prices. Despite this "lack of truthfulness" the vendor of the product might still decide to pay you for recommendation e.g. because she wants to (i) provide you with an additional incentive to actually recommend her or to (ii) increase your satisfaction and/or brand loyalty. This leads us to investigate a pricing scheme based on the Shapley value [5] that satisfies certain "axioms of fairness". We find that it is vulnerable to manipulations and show how to overcome these difficulties using the anonymity-proof Shapley value of [4].