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Location Aware Keyword Query Suggestion Based on Document Proximity
Keyword suggestion in web search helps users to access relevant information
without having to know how to precisely express their queries. Existing keyword
suggestion techniques do not consider the locations of the users and the query
results; i.e., the spatial proximity of a user to the retrieved results is not taken as a
results in many applications (e.g., location-based services) is known to be correlated
with their spatial proximity to the query issuer. In this paper, we design a locationaware keyword query suggestion framework. We propose a weighted keyworddocument graph, which captures both the semantic relevance between keyword
queries and the spatial distance between the resulting documents and the user
location. The graph is browsed in a random-walk-with-restart fashion, to select the
keyword queries with the highest scores as suggestions. To make our framework
scalable, we propose a partition-based approach that outperforms the baseline
an order of magnitude. The appropriateness of our framework and the performance
of the algorithms are evaluated using real data.
Keyword suggestion (also known as query suggestion) has become one of the most
fundamental features of commercial web search engines. After submitting a
keyword query, the user may not be satisfied with the results, so the keyword
suggestion module of the search engine recommends a set of m keyword queries that
are most likely to refine the user’s search in the right direction. Effective keyword
suggestion methods are based on click information from query logs and query
session data or query topic models. New keyword suggestions can be determined
according to their semantic relevance to the original keyword query. However, to
our knowledge, none of the existing methods provide location-aware keyword query
suggestion (LKS), such that the suggested queries retrieve documents not only
related to the user information needs but also located near the user location.
Disadvantages of Existing System:
1. Existing keyword suggestion techniques do not consider the locations of the
users and the query results
In this paper, we design the first ever Location-aware Keyword query Suggestion
framework, for suggestions relevant to the user’s information needs that also retrieve
relevant documents close to the query issuer’s location. We extend the state-of-theart Bookmark Coloring Algorithm (BCA) for random walk with restart (RWR)
search to compute the location-aware suggestions. In addition, we propose a
partition-based algorithm (PA) that greatly reduces the computational cost of BCA.
We conduct an empirical study that demonstrates the usefulness of location-aware
keyword query suggestion. We also show experimentally that PA is two times to one
order of magnitude faster than BCA.
Advantages of Proposed System:
1. The proposed framework can offer useful suggestions and that PA
outperforms the baseline algorithm significantly.
2. Reduce the Computational cost by using Partition-based algorithm
Hardware Configuration
 Processor
Pentium –IV
 Speed
1.1 Ghz
256 MB(min)
 Hard Disk
20 GB
 Key Board
Standard Windows Keyboard
 Mouse
 Monitor
Two or Three Button Mouse
Software Configuration
 Operating System
: Windows XP
 Programming Language
: C#
 R. Baeza-Yates, C. Hurtado, and M. Mendoza, “Query recommendation using
query logs in search engines,” in Proc. Int. Conf. Current Trends Database
Technol., 2004, pp. 588–596.
D. Beeferman and A. Berger, “Agglomerative clustering of a search engine
query log,” in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discovery Data
Mining, 2000, pp. 407–416.
H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li, “Context-aware
query suggestion by mining click-through and session data,” in Proc. 14th
ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 875–
N. Craswell and M. Szummer, “Random walks on the click graph,” in Proc.
30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2007, pp.
 Q. Mei, D. Zhou, and K. Church, “Query suggestion using hitting time,” in
Proc. 17th ACM Conf. Inf. Knowl. Manage., 2008, pp. 469–478.

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