1. Clustering Personalized Web Search Results Xuehua Shen and Hong Cheng

2. Introduction Search engine’s objectives –Rank most relevant search results at top Effectiveness PageRank / HITS –Group and present different categories of search results Global view Clustering

3. Clustering Personalized Search Results Study the clustering problem in the UCAIR framework Personalized search ranks or reranks the search results based on user implicit feedback Bring interesting problems –Efficient and effective clustering/presentation –Dynamically update the clustering results based on personalization

4. Goal Effective –Cluster user search results into meaningful groups –Present in a clear format –Provide users with main themes of search results Efficient –Implement efficient clustering algorithms Dynamic –Dynamically maintain the clustering results based on personalized ranking and reranking

5. Progress Implemented two clustering algorithms –K-Medoids –Hierarchical clustering Presentation –Replace Google ads with clustering results –Present ranked results together with clustering results –Two presentation strategies Most centrally located document in each cluster Most frequent terms in each cluster

6. Partial Results K-Medoids –Select the most centrally located documents as cluster center –Present the centroid documents as each cluster’s representative –Efficiency not so good Other processing time: 490+100+1562=2152 ms Cluster search results time: 2844 ms

7. Partial Results (II) Hierarchical clustering –Merge similar documents in a pair-wise manner –Use weighted average term vectors to represent cluster center –Present centroid term vectors as a virtual documents (output Top-K terms) –Efficiency better than K-Medoids Other processing time: 200+110+831= 1141 ms Cluster search results time: 661 ms

8. Efficiency Analysis K-Medoids –O(k(n-k) 2 ) for each iteration where n is # of documents, k is # of clusters –Need multiple iterations for convergence Hierarchical clustering –O(n 2 ) for each iteration –Need n-k iterations

9. Lessons Learned Clustering takes longer time as more search results accumulate (when we click “Next”) Top-K frequent terms in each cluster sometimes do not make sense –Combine additional information besides term frequency Re-cluster each time when reranking search results –Incremental update of clustering results is desired!

10. Remaining Implementation –KMeans –MMR –Frequent word sets Effective presentation study –Based on user feedback –Literature survey Dynamic maintenance of clustering based on search result ranking and reranking –Drill down in a particular cluster –Update overall clustering organization

11. Feedback Which way to present clustering results is more meaningful? –Based on central documents –Based on term vectors –More options? Any other clustering algorithms to achieve effectiveness and efficiency? Any other presentation strategy besides “rank list + cluster center” ?