Inferring user search goals by Clustering Pseudo Documents

A novel approach has been proposed to infer user search goals for a query by clustering its feedback sessions represented by pseudo-documents. The introduce feedback sessions to be analyzed to infer user search goals rather than search results or clicked URLs. Both the clicked URLs and the un clicked ones before the last click are considered as user implicit feedbacks and taken into account to construct feedback sessions. The feedback sessions can reflect user information needs more efficiently. Second, we map feedback sessions to pseudo documents to approximate goal texts in user minds. The pseudo-documents can enrich the URLs with additional textual contents including the titles and snippets. Based on these pseudo-documents, user search goals can then be discovered and depicted with some keywords. A new criterion CAP is formulated to evaluate the performance of user search goal inference. Experimental results on user click-through logs from a commercial search engine demonstrate the effectiveness of our proposed methods. the proposed pseudo-documents, we can infer user search goals. After clustering all the pseudo-documents, each cluster can be considered as one user search goal. The center point of a cluster is computed as the average of the vectors of all the pseudo-documents in the cluster, the terms with the highest values in the center points are used as the keywords to depict user search goals. Note that an additional advantage of using this keyword based description is that the extracted keywords can also be utilized to form a more meaningful query in query recommendation and thus can represent user information needs more effectively. Since we can get the number of the feedback sessions in each cluster, the useful distributions of user search goals can be obtained simultaneously. The ratio of the number of the feedback sessions in one cluster and the total number of all the feedback sessions is the distribution of the corresponding user search goal.