Name
Abstract | ||
|---|---|---|
We study the patterns by which a user consumes the same item repeatedly over time, in a wide variety domains ranging from check-ins at the same business location to re-watches of the same video. We find that recency of consumption is the strongest predictor of repeat consumption. Based on this, we develop a model by which the item from $t$ timesteps ago is reconsumed with a probability proportional to a function of t. We study theoretical properties of this model, develop algorithms to learn reconsumption likelihood as a function of t, and show a strong fit of the resulting inferred function via a power law with exponential cutoff. We then introduce a notion of item quality, show that it alone underperforms our recency-based model, and develop a hybrid model that predicts user choice based on a combination of recency and quality. We show how the parameters of this model may be jointly estimated, and show that the resulting scheme outperforms other alternatives. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2566486.2568018 | WWW |
Keywords | Field | DocType |
exponential cutoff,recency-based model,item quality,reconsumption likelihood,power law,repeat consumption,hybrid model,resulting scheme,business location,user choice | Data mining,Exponential function,Computer science,Cutoff,Ranging,Power law | Conference |
Citations | PageRank | References |
28 | 0.98 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashton Anderson | 1 | 593 | 28.11 |
| Ravi Kumar | 2 | 13932 | 1642.48 |
| Andrew Tomkins | 3 | 9388 | 1401.23 |
| Sergei Vassilvitskii | 4 | 2750 | 139.31 |