Personalized Recommendation Algorithm Based on Situation Awareness

With the increasing number of mobile devices and the development of all kinds of mobile technology, the volume of data is getting larger and larger. People have to face huge amounts of data. At the same time, as the continuous development of various mobile devices providing more data acquisition technology, the dimension of data have a bigger growth too. It's not enough for the existing technology in the treatment of huge amounts and high dimension data. User's interests in different time have been taken into equal consideration with the method being used, which leads to lack of effectiveness in the given period of time. In order to solve this problem, personalized recommendation is faced with great challenge. What's more, timeliness which is getting more and more urgent in people's need is very important. Towards the problem of recommendation based on context-aware, this paper propose a personalized recommendation algorithm based on user context clustering, with the consideration of timeliness. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users' context in history data set first to construct the user set which is similar with current user context, and then combined with user-based collaborative filtering algorithm for personalized recommendation.