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

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

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

4. 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.

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

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

7. 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).

8. 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.]

9. 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.]

10. 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

11. 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

12. 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

13. 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.

14. Experiments Metrics: Competitors:
Coverage: This is the percentage of input queries that can be served with at least one recommendation
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]

15. Experiments
Results:
coverage results:

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

17. Q&A