Location Aware Keyword Query Suggestion Based on Document Proximity

   December 8, 2017    Comments Off on Location Aware Keyword Query Suggestion Based on Document Proximity    Posted in: .Net, IEEE 2017

Location Aware Keyword Query Suggestion Based on Document Proximity

 

ABSTRACT

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 factor in the recommendation. However, the relevance of search
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 location-aware keyword query suggestion framework. We propose a weighted keyword-document 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 algorithm by up to
an order of magnitude. The appropriateness of our framework and the performance of the algorithms are evaluated using real data.

EXISTING SYSTEM

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

PROPOSED SYSTEM

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-the-art 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

MODULES

In this project, we have 3 main modules;

  1. KD-Graph Construction Module
  2. Partition Algorithm Module
  3. Selecting keyword Query Suggestion Module

Module Description:

Keyword-Document (KD) Graph Construction:

In Location-aware Keyword query Suggestion (LKS) framework constructs an initial keyword-document graph (KD-graph). This directed weighted bipartite graph between Documents and Keyword queries captures the semantics and textual relevance between the keyword query and document nodes; i.e., the first criterion of location-aware suggestion.

Partition Algorithm:

In this partition algorithm, it will divide the keyword queries and documents in the KD-Graph into groups. By doing this, we can improve the performance of the Baseline algorithm.

Selecting keyword Query Suggestion:

In this module, we have to select the suggestions i.e., after adjusting the weights for KD-graph based on the query location we have two selection suggestions those are relevance to the keyword query and closeness to the query location. The suggestions means here, which nodes having highest scores in the query graph those nodes are the suggestions.

SYSTEM CONFIGURATION

Hardware Configuration

·         Processor                                 –           Pentium –IV

  • Speed –           1 Ghz
  • RAM –           256 MB(min)
  • Hard Disk –           20 GB
  • Key Board –           Standard Windows Keyboard
  • Mouse –           Two or Three Button Mouse
  • Monitor –           SVGA

Software Configuration

  • Operating System : Windows XP
  • Programming Language : JAVA

 

 

  

Comments are closed.