Effective personalized mobile search using KNN
Title | Effective personalized mobile search using KNN |
Publication Type | Conference Paper |
Year of Publication | 2014 |
Authors | Swati, K., Patankar, A.J. |
Conference Name | Data Science Engineering (ICDSE), 2014 International Conference on |
Date Published | Aug |
Keywords | Android 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. |
DOI | 10.1109/ICDSE.2014.6974629 |
Citation Key | 6974629 |
- KNN
- Vectors
- user preferences
- user preference privacy
- Servers
- search engines
- personalized mobile search
- personalization
- pattern classification
- Ontology
- Ontologies
- mobile search engine
- mobile computing
- Mobile communication
- location search
- Android based mobile
- k nearest neighborhood
- information retrieval
- encryption techniques
- data privacy
- data maintaining security
- Cryptography
- concept extraction
- concept
- client-server systems
- client-server architecture
- clickthrough data
- Classification algorithms
- classification
- Androids