Name
Abstract | ||
|---|---|---|
Event sequences, such as patients' medical histories or users' sequences of product reviews, trace how individuals progress over time. Identifying common patterns, or progression stages, in such event sequences is a challenging task because not every individual follows the same evolutionary pattern, stages may have very different lengths, and individuals may progress at different rates. In this paper, we develop a model-based method for discovering common progression stages in general event sequences. We develop a generative model in which each sequence belongs to a class, and sequences from a given class pass through a common set of stages, where each sequence evolves at its own rate. We then develop a scalable algorithm to infer classes of sequences, while also segmenting each sequence into a set of stages. We evaluate our method on event sequences, ranging from patients' medical histories to online news and navigational traces from the Web. The evaluation shows that our methodology can predict future events in a sequence, while also accurately inferring meaningful progression stages, and effectively grouping sequences based on common progression patterns. More generally, our methodology allows us to reason about how event sequences progress over time, by discovering patterns and categories of temporal evolution in large-scale datasets of events. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2566486.2568044 | WWW |
Keywords | Field | DocType |
common set,common progression pattern,medical history,event sequences progress,time-evolving event sequence,grouping sequence,general event sequence,future event,event sequence,common pattern,common progression stage,user modeling,time series | Data mining,Market segmentation,Computer science,User modeling,Scalable algorithms,Artificial intelligence,Product reviews,Machine learning,Generative model | Conference |
Citations | PageRank | References |
26 | 1.11 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaewon Yang | 1 | 1680 | 60.63 |
| Julian John McAuley | 2 | 2856 | 115.30 |
| Jure Leskovec | 3 | 18769 | 886.50 |
| Paea LePendu | 4 | 294 | 21.32 |
| Nigam Shah | 5 | 212 | 20.11 |