The exponential explosion of various contents on the Web, made Recommendation Systems increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc. The proposed system uses the historical browsers data for search key words and provides users with most relevant web pages. All the users’ click-through activity such as number of times he visited, duration he spent, his mouse movements and several other variables are stored in database. The proposed system uses this database and process to rank them. We have proposed a Radial Basis Function Neural Network [RBFNN]. The results obtained using the standard measures like precision, coverage and F1 measure on the proposed technique, produces the most relevant results as compared to aggregation technique based method and iPACT method. The RBFNN algorithm shows better prediction precision, coverage and the F1 measure than the iPACT method. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including expert finding, image recommendations, image annotations etc.

Image Recommendation, Neural Network, Query Suggestion, Recommendation System, Webpage Recommendation

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