Dynamic Personalized Recommendation on sparse Data

Abstract

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.

Existing System

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then user these relationships to indirectly compute recommendations for users.

Proposed System

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. k-means clustering algorithm and discuss several alternatives. This work present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied. This improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.

Architecture

System Architecture