Inferring User Image-Search Goals Under the Implicit Guidance of Users
The analysis of user search goals for a query can be very useful in improving search engine relevance and user experience. Although the research on inferring user goals or intents for text search has received much attention, little has been proposed for image search. In this paper, we propose to leverage click session information, which indicates high correlations among the clicked images in a session in user click-through logs, and combine it with the clicked images' visual information for inferring user image-search goals. Since the click session information can serve as past users' implicit guidance for clustering the images, more precise user search goals can be obtained. Two strategies are proposed to combine image visual information with the click session information. Furthermore, a classification risk based approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on a popular commercial search engine demonstrate the effectiveness of the proposed method.