Abstract
Although several recommendation algorithms are widely used by both commercial and non-commercial platforms, they face unique challenges such as sparse data sets and the absence of negative or “neutral” feedback. One-class algorithms attempt to overcome the data sparsity problem by using the implicit feedback inherent in user’s clicks and purchases, which are deduced from both positive and negative feedback. Existing literature uses several heuristic strategies to derive the negative samples needed for training, such as using random sampling or utilizing user-item interaction. However, these assumptions do not always reflect reality. In addition, with the explosive increase in the availability of big data for training recommendation systems, these methods might not adequately encapsulate the representations of the latent vectors. In this paper, we address the common issues of one-class recommendation and provide a survey on approaches that have been used to mitigate the existing challenges. To tackle the identified problems, we propose a neural network-based Bayesian Personalized Ranking (BPR) for item recommendation and personalized ranking from implicit feedback. BPR provides an optimization criterion derived from Bayesian analysis of a problem to develop an optimized model for such a problem. We conduct several experiments on two varieties of MovieLens datasets to illustrate the performance of the proposed approach. Our approach shows an impressive result in mitigating the issues of one-class recommendation when compared with the complexity of the state-of-the-art methods.
Highlights
Recommender systems have evolved and are typically classified according to their application domains [1] or whether they are generally applicable to different domains
This study focuses on the application of matrix factorization to one class recommendation problem in the context of simplicity and the ability to solve the problem at hand
This study examines how matrix factorization methods can be applied to implicit feedback in the one-class recommendation problem
Summary
Many companies have realized the importance of predictive analysis methods facilitated with machine learning, especially the remarkable results from deep learning algorithms. This creates a justification for the wide adoption and growing interest in the use of recommender systems by e-commerce companies to boost sales. Ko et al [1] identified the application domains of recommender systems as streaming service, social network service, tourism service, e-commerce service, healthcare service, education service, and academic information service. A recommendation system is guiding you towards the most likely product you might purchase using your interaction history. Recommender systems have evolved and are typically classified according to their application domains [1] or whether they are generally applicable to different domains
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