Multiple phase division is all normalized rating Values

A novel dynamic personalized recommendation algorithm for sparse data, in which more rating data is utilized in one prediction by involving more neighboring ratings through each attribute in user and item profiles. A set of dynamic features are designed to describe the preference information based on TSA technique, and finally a recommendation is made by adaptively weighting the features using information in multiple phases of interest. The drifting of users’ preferences or items’ reputations is not too rapid, which makes it possible to describe temporal state of them by using some features. In this section, firstly we introduce a way to make use of profiles to extend the co-rating relation, and then we propose a set of dynamic features to reflect users’ preferences or items’ reputations in multiple phases of interest, and after that we propose an adaptive algorithm for dynamic personalized recommendation. The internet has become an indispensable part of our lives, and it provides a platform for enterprises to deliver information about products and services to the customers conveniently. As the amount of this kind of information is increasing rapidly, one great challenge is ensuring that proper content can be delivered quickly to the appropriate customers. Personalized recommendation is a desirable way to improve customer satisfaction and retention. There are mainly three approaches to recommendation engines based on different data analysis methods, i.e., rule-based, content-based and collaborative filtering. Among them, collaborative filtering (CF) requires only data about past user behavior like ratings, and its two main approaches are the neighborhood methods and latent factor models. The neighborhood methods can be user-oriented or item-oriented. They try to find like-minded users or similar items on the basis of co-ratings, and predict based on ratings of the nearest neighbors. Latent factor models try to learn latent factors from the pattern of ratings using techniques like matrix factorization and use the factors to compute the usefulness of items to users. CF has made great success and been proved to perform well in scenarios where user preferences are relatively static.