A Large-scale Evaluation and Analysis of Personalized Search Strategies
- Zhicheng Dou (Nankai University, China)
- Ruihua Song (Microsoft Research Asia)
- Ji-Rong Wen (Microsoft Research Asia)
Although personalized search has been proposed for many years and many personalization strategies 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 the problem and get some preliminary conclusions. We present a large-scale personalized search evaluation framework based on search logs and then evaluate five personalized search strategies (including two click-based and three profile-based ones) using 12-day MSN search logs. By analyzing the results, we reveal that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries (e.g., queries with small click entropy) and even harms search accuracy under some situations. Furthermore, we show that click-based personalization strategies perform consistently and considerablely well while profile-based ones are unstable in our experiments. We also reveal that both long-term and short-term contexts are very important in improving search performance for profile-based personalized search strategies.