Name
Abstract | ||
|---|---|---|
Recommendation methods have mainly dealt with the problem of recommending new items to the user while user visitation behavior to the familiar items (items which have been consumed before) are little understood. In this paper, we analyze user activity streams and show that user's temporal consumption of familiar 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) recommending items that are not in the bored state for the user, (2) recommending items where user has restored her interests. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2684822.2685306 | WSDM |
Keywords | Field | DocType |
boredom,activity streams,information filtering,dynamic preferences,temporal recommender systems,data mining | Information retrieval,Computer science,Boredom | Conference |
Citations | PageRank | References |
18 | 0.73 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Komal Kapoor | 1 | 85 | 4.60 |
| Karthik Subbian | 2 | 263 | 17.58 |
| Jaideep Srivastava | 3 | 5845 | 871.63 |
| Paul R. Schrater | 4 | 141 | 22.71 |