It is true that one of the significant aims of building content recommendation is the construction

of person ahead user profiles. In this case the two problems of feature engineering and the proper

utilization of the signals using data mining and machine learning approaches are faced.

            This paper present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. The five contextual information sources used are: social, historic, task, collection, and user interaction. This study focus on website recommendations rather than search results. This research evaluate the utility of these five sources, and overlaps between them, based on how effectively they predict users’ future interests. The results demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source.