Just in time recommendations - Modeling the dynamics of boredom in activity streams

Recommendation methods have mainly dealt with the problem of recommending new items to the user while user visitation be-havior to the familiar items (items which have been consumed before) are little understood. In this paper, we analyze user ac-tivity streams and show that user's temporal consumption of fa-miliar items is driven by boredom. Specifically, users move on to a different item when bored and return to the same item when their interest is restored. To model this behavior we include two latent psychological states of preference for items - sensitization and boredom. In the sensitization state the user is highly engaged with the item, while in the boredom state the user is disinterested. We model this behavior using a Hidden Semi-Markov Model for the gaps between user consumption activities. We show that our model performs much better than the state-of-the-art temporal recommendation models at predicting the revisit time to the item. Moreover, we attribute two main reasons for this: (1) recom-mending items that are not in the bored state for the user, (2)recommending items where user has restored her interests.