Search behavior-driven training for result re-ranking
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Proceedings of the 20th ACM international conference on Information and knowledge management
A unified context model for web image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
On Finding Fine-Granularity User Communities by Profile Decomposition
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A personalised search approach for web service recommendation
International Journal of Ad Hoc and Ubiquitous Computing
Personalised Information Retrieval: survey and classification
User Modeling and User-Adapted Interaction
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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.