Visible to the public Effective personalized mobile search using KNN

TitleEffective personalized mobile search using KNN
Publication TypeConference Paper
Year of Publication2014
AuthorsSwati, K., Patankar, A.J.
Conference NameData Science Engineering (ICDSE), 2014 International Conference on
Date PublishedAug
KeywordsAndroid based mobile, Androids, classification, Classification algorithms, clickthrough data, client-server architecture, client-server systems, concept, concept extraction, cryptography, data maintaining security, data privacy, encryption techniques, information retrieval, k nearest neighborhood, KNN, location search, Mobile communication, mobile computing, mobile search engine, Ontologies, Ontology, pattern classification, personalization, personalized mobile search, search engines, Servers, user preference privacy, user preferences, Vectors
Abstract

Effective Personalized Mobile Search Using KNN, implements an architecture to improve user's personalization effectiveness over large set of data maintaining security of the data. User preferences are gathered through clickthrough data. Clickthrough data obtained is sent to the server in encrypted form. Clickthrough data obtained is classified into content concepts and location concepts. To improve classification and minimize processing time, KNN(K Nearest Neighborhood) algorithm is used. Preferences identified(location and content) are merged to provide effective preferences to the user. System make use of four entropies to balance weight between content concepts and location concepts. System implements client server architecture. Role of client is to collect user queries and to maintain them in files for future reference. User preference privacy is ensured through privacy parameters and also through encryption techniques. Server is responsible to carry out the tasks like training, reranking of the search results obtained and the concept extraction. Experiments are carried out on Android based mobile. Results obtained through experiments show that system significantly gives improved results over previous algorithm for the large set of data maintaining security.

DOI10.1109/ICDSE.2014.6974629
Citation Key6974629