An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Information Systems (TOIS)
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
RecMax: exploiting recommender systems for fun and profit
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
When power users attack: assessing impacts in collaborative recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Accuracy and robustness impacts of power user attacks on collaborative recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
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In this paper we propose a market-based approach for seeding recommendations for new items in which publishers bid to have items presented to the most influential users for each item. Users are able to select (or not) items for rating on an earn-per-rating basis, with payment given for providing a rating regardless of whether the rating is positive or negative. This approach reduces the time taken to obtain ratings for new items, while encouraging users to give honest ratings (to increase their influence) which in turn supports the quality of recommendations. To support this approach we propose techniques for inferring the social influence network from users' rating vectors and recommendation lists. Using this influence network we apply existing heuristics for estimating a user's influence, adapting them to account for the new items already presented to a user. We also propose an extension to Chen et al.'s Degree Discount heuristic [Chen et al. 2009], to enable it to be used in this context. We evaluate our approach on the MovieLens dataset and show that we are able to reduce the time taken to achieve coverage, while supporting the quality of recommendations.