Abstract
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.
Original language | English (US) |
---|---|
Title of host publication | WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining |
Publisher | Association for Computing Machinery |
Pages | 233-242 |
Number of pages | 10 |
ISBN (Electronic) | 9781450333177 |
DOIs | |
State | Published - Feb 2 2015 |
Event | 8th ACM International Conference on Web Search and Data Mining, WSDM 2015 - Shanghai, China Duration: Jan 31 2015 → Feb 6 2015 |
Publication series
Name | WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining |
---|
Other
Other | 8th ACM International Conference on Web Search and Data Mining, WSDM 2015 |
---|---|
Country/Territory | China |
City | Shanghai |
Period | 1/31/15 → 2/6/15 |
Bibliographical note
Publisher Copyright:Copyright © 2015 ACM.