Name
Abstract | ||
|---|---|---|
When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that on-task context is helpful on average but can be easily overwhelmed when off-task queries are interleaved---a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context. We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2484028.2484069 | SIGIR |
Keywords | Field | DocType |
task-aware query recommendation,personalized web search,current search context,recent search interaction,arbitrary user search behavior,on-task query,commercial search log,automatic search task identification,art search task identification,reference query,off-task query | Query optimization,Web search query,Query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Spatial query,Online aggregation | Conference |
Citations | PageRank | References |
15 | 0.62 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henry A. Feild | 1 | 329 | 18.08 |
| James F. Allen | 2 | 9929 | 1631.65 |