Web page recommendations using Radial Basis Neural Network technique

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 keywords 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 proposed technique produces the most relevant results as compared to aggregation technique based method. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including expert finding, image recommendations, image annotations, etc. The experimental results show the promising future of our work.