Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.
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.
In this paper, we present a novel hybrid dynamic recommendation approach. Firstly, in order to utilize more information while keeping data consistency, we use user profile and item content to extend the co-rate relation between ratings through each attribute.
The main contributions of this paper can be summarized as follows:
(a) More information can be used for recommender systems by investigating the similar relation among related user profile and item content.
(b) A novel set of dynamic features is proposed to describe users’ preferences, which is more flexible and convenient to model the impacts of preferences in different phases of interest compared with dynamic methods used in previous works, since the features are designed according to periodic characteristics of users’ interest and a linear model of the features can catch up with changes in user preferences.
(c) An adaptive weighting algorithm is designed to combine the dynamic features for personalized recommendation, in which time and data density factors are considered to adapt with dynamic recommendation on sparse data.
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