Name
Abstract | ||
|---|---|---|
Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TKDE.2008.172 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
windows live search,personalized web search,personalization,12-day query log,different search context,personalization algorithm,query log,topical-interest-based personalized search,information filtering,click-based personalized search,web search,personalized search algorithm,generic web search,query performance,internet,different query,large-scale evaluation framework,query logs,performance evaluation.,personalized search,click entropy,performance evaluation,query processing,history,predictive models,prediction model,search engines,rodents,entropy,computer peripherals | Data mining,Search algorithm,Personalized search,Computer science,Web query classification,Artificial intelligence,Personalization,Web search query,Search engine,Information retrieval,Query expansion,Information extraction,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 8 | 1041-4347 |
Citations | PageRank | References |
23 | 0.91 | 47 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhicheng Dou | 1 | 706 | 41.96 |
| Ruihua Song | 2 | 1138 | 59.33 |
| Ji-Rong Wen | 3 | 4431 | 265.98 |
| Xiao-Jie Yuan | 4 | 255 | 34.96 |