Large amount of information is available on web and information extraction takes place in huge volume. When queries are submitted to search engines they are generally in natural languages and contain just one or two words. Search engine are unable to recognize natural language and thus it becomes difficult to extract the proper information from web according to user's interest. Here, the recommendation technique comes into picture. There are number of recommendation techniques, every technique has its advantages and disadvantages. Recommendation techniques are designed in such a way that they support various type or data sources. These data sources are in the form of text, images, audio, video etc. Efficient way to deal with all types of data sources is, model them in the form of graph and then apply recommendation algorithm on it. Initially the proposed system collects data from user's and graphs are constructed by using this data. Subsequently the system uses different algorithms for predicting user's interest. The algorithms are item to item base collaborative filtering algorithm, Pearson correlation base collaborative filtering algorithm. These are applied for finding similarities between item and users respectively. Slope one algorithm is used to find out the rating of un-rated items. In proposed hybrid method results of these algorithms are combined. The hybridization of Algorithms leads to efficient results.

Recommendation system Web mining web graph personalization feature.