Open AccessAn Effective and Trustable Spatial Service Recommendation Algorithm for Spatial Query Retrieval In Geo-Social Network
Author(s) -
K. Lakshmaiah,
S. Murali Krishna,
B. Eswara Reddy
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v12.i3.pp1222-1229
Subject(s) - computer science , information retrieval , geospatial analysis , spatial analysis , spatial database , spatial query , data retrieval , set (abstract data type) , data mining , precision and recall , query expansion , search engine , web query classification , web search query , remote sensing , geography , programming language
The spatial information (e.g., restaurants/hotels) is related with the keyword(s) to indicate their businesses, services and features. The main issue of relevant information retrieval is to query an entity which includes a set of spatial query keywords and have the smallest amount of inter-object distance. The spatial queries with keywords have not been extensively explored. Still, the traditional method was focused on the multidimensional data. Previous works mostly targeted to predict the top-k Nearest Neighbors keyword query, where every keyword should be equivalent to the whole querying keywords. However, the mechanism does not consider the density of data entities in the spatial space. To overcome the above issues, An Effective and Trustable Spatial Service Recommendation (ETSSR) algorithm focuses on the most relevant information retrieval with the enhanced accuracy and minimal retrieval time for spatial information services. The main goal of work is to provide best spatial information retrieval with an accurateness of location prediction and minimal information retrieval time. The system minimizes the classification issue and visualization problem for spatial information in Geospatial Social network. The system improves the spatial information retrieval with an accuracy of location prediction and minimizes the information retrieval time compare than existing methods. Based on Experimental estimations, proposed ETSSR+KNN enhanced 0.48 P (Precision) and 0.49 R (Recall) and minimized 28 milliseconds query retrieval time.