“Sources of information on specific subjects”
Journal of Information Science - Lecture notes in computer science, No. 207
Strategic bidder behavior in sponsored search auctions
Decision Support Systems
INFORMS Journal on Computing
Authority and ranking effects in data fusion
Journal of the American Society for Information Science and Technology
Popularity, novelty and attention
Proceedings of the 9th ACM conference on Electronic commerce
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We analyze the music charts of an online digital music distributor that displays real time and weekly rankings on its website, and study how ranking policy should be set to maximize the value of its online music ranking service. The existing mechanism considers only streaming and download volumes, while the new ranking mechanism reflects more accurate preferences for popularity, pricing policy, and the slot effect based on the exponential decay of attention. The new ranking model is designed to verify correlations with two kinds of service volumes for popularity, pricing policy, and the slot effect. Slot mechanism design is analyzed in an heuristic way. Our analysis shows that music content sellers maximize benefits by assigning their own music items to the highest-ranking slot, which provides visibility. Also sellers can strategically design the slot size to influence the popularity of music items. Music content buyers gain indirect benefits by getting segmented ranking slots and reducing search costs. Empirical analysis illustrates the features of the online music industry and validates hypotheses constructed around the new ranking model. The results show that the new ranking mechanism is more effective.