.png){kind=icon}
.png){kind=icon}
Return
Deriving Concept-Based User Profiles from Search Engine Logs
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2009.144.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.
Keywords:
Negative preferences
personalization
personalized query clustering
search engine
user profiling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.7K
Citations:
3.2W
Organization
No organization information available