a user feed back approach for recommendation in personalized mobile search
N Jagadish Kumar,
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:2 Year: 08 March,2014 Pages:112-119
Abstract
Mobile device interaction with users serves various purposes such as a location service, road map, traffic information, etc. It also helps users connect to the search engines. But the search query is limited to small words, unlike those used when interacting with search engines through computers. In most cases the information retrieved through mobile search is also not relevant to the user query. This leads to a major drawback for the user to communicate through mobile devices to the web server, as there are limitations in the form factor of mobile devices. To overcome this issue in mobile search several researchers have been conducted to build a solution to personalize the mobile search. Personalized mobile search is an effective way to retrieve query result for each user according to his/her interest. The existing personalized mobile search focused only on ontology based search and location based search to retrieve the relevant web document which is not user preference. Hence the objective of the proposed work is to integrate a user feedback mechanism to the existing personalized mobile search. Here the feedback given by the user in the form of ratings for the particular personalized search result is considered as the opinion of the user. With this opinion user’s interest in particular search is analyzed and the user is collaborated with the similar interest users, who comes under same age and gender category. Thus the personalized mobile search proposed in this work will be able to provide user preference search based on his/her opinion and feedback search based on the opinion of another user with similar age, gender and interest.
Kewords
Personalization, User profile, User interest, Content mining, Location mining, Ontology mining, Re-ranking.
Reference
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