Location-Aware Query Recommendation for Search Engines at Scale Zhipeng Huang, The University of Hong Kong Nikos Mamoulis, University of Ioannina

Query Recommendation Suggest important and useful query alternatives to search engine users. “SSTD”

Location-Related Query Suppose you want to find a KFC in the nearby, We need better location-aware query recommendation!

Location from query issuer Dropping price of Smartphones Increasing coverage and bandwidth of mobile networks Leading to more and more search engine queries from mobile users, which have location information as an important feature. We study using the location information to improve the quality of query recommendation.

Location of a Web page Distribution got from frequency of mentioned locations.

Location of a query Query-Document bipartite graph Location Aggregation: Q D

Spatial similarity Given a search engine user u, the spatial similarity between him and a Web query q is: Where r is a range threshold (by default 100km).

Existing Recommendation models QFG (Query-Flow Graph) Recommendation: do a PPR from the input query, and recommend those with large PPR scores (or probabilities). [P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: model and applications. In CIKM , pages 609–618. ACM, 2008.]

Existing Recommendation models TQGraph (Term-Query Flow Graph) Recommendation: do a PPR from each term, and aggregate the PPR scores. [F. Bonchi, R. Perego, F. Silvestri, H. Vahabi, and R. Venturini. Efficient query recommendations in the long tail via center-piece subgraphs. In SIGIR, pages 345–354. ACM, 2012.]

Location-aware PPR Adjust the weights according to the spatial similarity between a query and the user: Then we do PPRs over the adjusted graph. Location-aware QFG Location-aware TQGraph

Computing PPR Efficiently Bookmark Coloring Algorithm (BCA) model the RWR process as a bookmark coloring process, in which some portion of the ink in a processed node is sent to its neighbors, while the remaining ink is retained at the node. (suppose ) 0.255 0.3 0.3 1.0 0.15 0.7 0.7 0.595

Lazy Update Mechanism In BCA, we need to maintain a priority query of nodes sorted by the amount of ink to be propagated. the pushing a node into the priority queue is delayed until the ink it receives is greater than a threshold. (suppose e = 0.1) Red: In queue Green: Not in queue 0.1 0.015 0.1 0.4 0.2 0.4 0.4 0.03 0.5 0.03 0.5 0.5 0.119 0.085

Spatial indeces Speed up the spatial similarity computing by calculating an approximation based on grids. We need to check at most cells, where a is the size of the cells.

Experiments Metrics: Competitors: Coverage: This is the percentage of input queries that can be served with at least one recommendation precision@k: spatialSim@k: the spatial similarity between the recommended queries to the location of user Competitors: LKS: a most recently location-aware keyword suggestion approach. SQFG: our location aware Query STQGraph: our location-aware Term-Query-Flow Graph approach. STQGraph*: approximation version. [S. Qi, D. Wu, and N. Mamoulis. Location aware keyword query suggestion based on document proximity. TKDE, 28(1):82–97, 2016]

Experiments Results: coverage results:

Experiments Running time: our default setting takes around 0.3s.

Q&A