Name
Abstract | ||
|---|---|---|
We present a novel language modeling approach to capturing the query reformulation behavior of Web search users. Based on a framework that categorizes eight different types of "user moves" (adding/removing query terms, etc.), we treat search sessions as sequence data and build n-gram language models to capture user behavior. We evaluated our models in a prediction task. The results suggest that useful patterns of activity can be extracted from user histories. Furthermore, by examining prediction performance under different order n-gram models, we gained insight into the amount of history/context that is associated with different types of user actions. Our work serves as the basis for more refined user models. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1148170.1148315 | SIGIR |
Keywords | Field | DocType |
action modeling,n-gram language model,refined user model,user move,web search user,novel language modeling approach,user action,different order n-gram model,query behavior,user behavior,user history,different type,user model,language model,modeling language | Data mining,Query language,Computer science,Web query classification,Modeling language,Natural language processing,User modeling,Artificial intelligence,Query optimization,Web search query,RDF query language,Information retrieval,Query expansion | Conference |
ISBN | Citations | PageRank |
1-59593-369-7 | 1 | 0.36 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Craig Murray | 1 | 185 | 10.80 |
| Jimmy Lin | 2 | 4800 | 376.93 |
| Abdur Chowdhury | 3 | 2013 | 160.59 |