GPU_MF_SGD: A Novel GPU-Based Stochastic Gradient Descent Method for Matrix Factorization
Recommender systems are used in most of nowadays applications. Providing real-time suggestions with high accuracy is considered as one of the most crucial challenges that face them. Matrix factorization (MF) is an effective technique for recommender syste
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Abstract. Recommender systems are used in most of nowadays applications. Providing real-time suggestions with high accuracy is considered as one of the most crucial challenges that face them. Matrix factorization (MF) is an effective technique for recommender systems as it improves the accuracy. Stochastic Gradient Descent (SGD) for MF is the most popular approach used to speed up MF. SGD is a sequential algorithm, which is not trivial to be parallelized, especially for large-scale problems. Recently, many researches have proposed parallel methods for parallelizing SGD. In this research, we propose GPU_MF_SGD, a novel GPU-based method for large-scale recommender systems. GPU_MF_SGD utilizes Graphics Processing Unit (GPU) resources by ensuring load balancing and linear scalability, and achieving coalesced access of global memory without preprocessing phase. Our method demonstrates 3.1X– 5.4X speedup over the most state-of-the-art GPU method, CuMF_SGD. Keywords: Collaborative filtering (CF) Matrix factorization (MF) GPU implementation Stochastic Gradient Descent (SGD)
1 Introduction Recently, recommender systems have become a popular tool used in various applications including Facebook, YouTube, Twitter, Email services, News services and Hotel reservation applications [1–5]. In recommender systems, users get a list of suggested items (i.e. movies, friends, news, advertisements, products, etc.). One of the most important challenges that face recommender systems is suggesting accurate recommendations in real-time [6–8]. Recommender systems can be categorized into non-personalized filtering, contentbased filtering (CBF), collaborative filtering (CF) and matrix factorization techniques [1, 3, 9–15]. Non-personalized recommender systems suggest items to a user based on average of ratings given to the items by other users. This category of recommender systems is trivial in terms of implementation, but it lacks personalization where recommended items are the same for all users regardless of their profile [1, 4, 13]. In CBF, items are suggested to a user based on items rated previously by the user. CBF represents items and users’ behaviors as features to find similarity between users’ © Springer Nature Switzerland AG 2019 K. Arai et al. (Eds.): FICC 2018, AISC 887, pp. 271–287, 2019. https://doi.org/10.1007/978-3-030-03405-4_18
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preferences and items. Defining features that represent items and users’ behaviors is a major problem in CBF [1, 14, 16]. CF is a process of suggesting items based on users’ collaboration or the similarity between items [1, 3, 9, 10]. CF overcomes the issue of CBF features representation. However, CF cannot suggest items when there are no similarities between users or items. Moreover, CF performs slowly on huge datasets [17–20]. MF, a dimensionality reduction technique, is an advanced technique for recommender systems where the representation of users and items uses the same latent features. Predicting ratings is simply performed by the inner product of user-item feature vect
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