Minimizing Service and Operation Costs of Periodic Scheduling
Mathematics of Operations Research
The role of compatibility in the diffusion of technologies through social networks
Proceedings of the 8th ACM conference on Electronic commerce
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The dynamics of repeat consumption
Proceedings of the 23rd international conference on World wide web
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Motivated by trends in popularity of products, we present a formal model for studying trends in our choice of products in terms of three parameters: (1) their innate utility; (2) individual boredom associated with repeated usage of an item; and (3) social influences associated with the preferences from other people. Different from previous work, in this paper we introduce boredom to explain the cyclic pattern in individual and social choices. We formally model boredom and show that a rational individual would make cyclic choices when considering the boredom factor. Furthermore, we extend the model to social choices by showing that a society that votes for a particular style or product can be viewed as a single individual cycling through different choices. We adopt a natural model of utility an individual derives from using an item, i.e., the utility of an item gets discounted by its repeated use and increases when the item is not used. We address the problem of optimally choosing items for usage, so as to maximize overall user satisfaction over a period of time. First we show that the simple greedy heuristic of always choosing the item with the maximum current composite utility can be arbitrarily worse than the optimal. Second, we prove that even with just a single individual, determining the optimal strategy for choosing items is NP-hard. Third, we show that a simple modification to the greedy algorithm is a provably close approximation to the optimal strategy. Finally, we present an experimental study over real-world data collected from query logs to compare our algorithms.