期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：8
页码：17414
DOI：10.15680/IJIRSET.2017.0608262
出版社：S&S Publications
摘要：The design proposes a weighted keyword-document graph, which captures both the semantic relevancebetween keyword queries and the spatial distance between the resulting documents and the user location. The graph isbrowsed in a random-walk-with-restart fashion to select the keyword queries with the highest scores as suggestions. Tomake our framework scalable, we propose a partition-based approach that outperforms the baseline algorithm by up toan order of magnitude. The appropriateness of our framework and the performance of the algorithms are evaluatedusing real data.Keyword suggestion techniques do not consider the locations of the users and the query results; i.e., the spatialproximity of a user to the retrieved results is not taken as a factor in the recommendation. However, the relevance ofsearch results in many applications (e.g., location-based services) they did not give the correct correlance. A baselinealgorithm extended from algorithm BCA is introduced to solve the problem. Then a partition-based algorithm (PA)which computes the scores of the candidate keyword queries at the partition level and utilizes a lazy mechanism togreatly reduce the computational cost. The performance of the algorithms is low.To providing keyword suggestions that are relevant to the user information needs and at the same time can retrieverelevant documents near ideas, but aims at optimizing different objective functions. The concept of prestige basedspatial keyword search. The SI-index comes with two query algorithms based on merging and distance browsingrespectively. To design a variant of inverted index that is optimized for multidimensional points, and is thus named theSpatial Inverted index (SI-index). To remedy the situation by developing an access method called the spatial invertedindex (SI-index). Not only that the SI-index is fairly space economical, but also it has the ability to perform keywordaugmented nearest neighbor search in time that is at the order of dozens of mille-seconds.Users often have difficulties in expressing their web search needs they may not know the keywords. After submitting akeyword query, the user may not be satisfied with the results, so that we can provide single keyword query and locationthen it calculate the distance based on the query and location using the fast nearest search and provide the results basedon user query and nearest to the location
