Abstract
In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.
Highlights
With the rapid development of the Internet applications and e-commerce, how to quickly and accurately recommend items to different users that they are interested in has become a critical focus and many researchers have been devoted to this area. e personalized recommendation system is an effective method to solve this problem
It mainly uses user behavior information to make recommendations. e model-based collaborative filtering (CF) is based on user preference information samples, training a recommendation model, and calculating and generating recommendation results based on real-time user preferences
Inspired by the literature [30,31,32], this paper proposes a new semisupervised support vector machine method based on active learning (AL) techniques, which can combine the advantages of these two algorithms to overcome the defects of TSVM and identify the samples that have the greatest impacts on classifier performance, significantly reducing the burden of users’ annotation task
Summary
With the rapid development of the Internet applications and e-commerce, how to quickly and accurately recommend items (including goods, news, services) to different users that they are interested in has become a critical focus and many researchers have been devoted to this area. e personalized recommendation system is an effective method to solve this problem. In order to solve the data sparsity problem, many researchers try to use a small number of labeled data and machine learning methods such as classification, clustering, and dimension reduction to enhance the dataset These methods have a common problem: when there are few labeled samples used in model construction, the prediction accuracy often is not high enough. Us, how to combine the limited labeled samples and a large number of unlabeled samples to build a “user-item” association relationship model to predict users’ interest preference for personalized recommendation has become an urgent issue. AL is interactively exploring the unknown information of unlabeled data according to certain strategies and labeling them with domain knowledge In this procedure, a small number of labeled “user-item” association data and a large number of unlabeled data are used to construct a model-based personalized recommendation; the ability to discover users’ potential preferences is improved. (4) AL selectively interacts with users and asks for information such as item ratings, which can supplement those aspects of interest with sparse data, helping the interest model to be comprehensive, and achieve a better performance of the recommendation system
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