applied sciences Article Deep Learning Architecture for Collaborative Filtering Recommender Systems Jesus Bobadilla * , Santiago Alonso and Antonio Hernando Dpto. Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain; [email protected] (S.A.); [email protected] (A.H.) * Correspondence: [email protected] Received: 17 March 2020; Accepted: 30 March 2020; Published: 3 April 2020 Abstract: This paper provides an innovative deep learning architecture to improve collaborative filtering results in recommender systems. It exploits the potential of the reliability concept to raise predictions and recommendations quality by incorporating prediction errors (reliabilities) in the deep learning layers. The underlying idea is to recommend highly predicted items that also have been found as reliable ones. We use the deep learning architecture to extract the existing non-linear relations between predictions, reliabilities, and accurate recommendations. The proposed architecture consists of three related stages, providing three stacked abstraction levels: (a) real prediction errors, (b) predicted errors (reliabilities), and (c) predicted ratings (predictions). In turn, each abstraction level requires a learning process: (a) Matrix Factorization from ratings, (b) Multilayer Neural Network fed with real prediction errors and hidden factors, and (c) Multilayer Neural Network fed with reliabilities and hidden factors. A complete set of experiments has been run involving three representative and open datasets and a state-of-the-art baseline. The results show strong prediction improvements and also important recommendation improvements, particularly for the recall quality measure. Keywords: collaborative filtering; reliabilities; deep learning; recommender systems; matrix factorization 1. Introduction Recommender Systems (RS) are powerful tools to address the information overload problem in the Internet. They make use of diverse sources of information. Explicit votes from users to items, and implicit interactions are the basis of the Collaborative Filtering (CF) RS. Implicit interactions examples are clicks, listened songs, watched movies, likes, dislikes, etc. Often, hybrid RS [1] are designed to ensemble CF with some other types of information filtering: demographic [2], context-aware [3], content-based [4], social information [5], etc. RS cover a wide range of recommendation targets, such as travels [6], movies [7], restaurants [8], fashion [9], news [10], etc. Traditionally, CF RS have been implemented using machine learning methods and algorithms, mainly the memory-based K nearest neighbor algorithm, and more recently the Matrix Factorization (MF) method. MF is an efficient model-based approach that obtains accurate recommendations; it is easy to understand and to implement, and it is based on the dimension reduction basis, where a reduced set of hidden factors contains the ratings information. Predictions are obtained by making the dot product of the users and the items hidden factors. There are several implementations of the MF such as PMF [11] and BNMF. An important drawback of the MF predictions is that the linear dot product cannot catch the complex non-linear relations existing among the set of hidden factors. Neural models do not have this restriction and they are called to lead the CF research in RS. Currently, RS research is directed toward deep learning approaches [12,13]. It is usual to find neural content-based approaches: images [8,9], text [10,14], music [15,16], videos [17,18], etc. The above targets Appl. Sci. 2020, 10, 2441; doi:10.3390/app10072441 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, x FOR PEER REVIEW 2 of 14 Appl. Sci. 2020, 10, 2441 2 of 14 Currently, RS research is directed toward deep learning approaches [12,13]. It is usual to find neural content-based approaches: images [8,9], text [10,14], music [15,16], videos [17,18], etc. The aboveare processed targets are by usingprocessed different by using neural different networks neur architectures:al networks Convolutionalarchitectures: Convolutional Neural Networks Neural [19], NetworksRecurrent Neural[19], Recurrent Networks Neural [20], Multilayer Networks Neural [20], Multilayer Networks Neural (MNN) Netw [3,21],orks and (MNN) autoencoders [3,21], [ 7and,22] autoencodersare usual approaches. [7,22] are Theusual preceding approaches. publications The preceding are focused publications on content-based are focused or on hybrid content-based filtering. orCF hybrid neural filtering. approaches CF canneural be classifiedapproaches as: can (1) be Deep classified Factorization as: (1) Deep Machines Factorization (deepFM) Machines [23] and (deepFM)(2) Neural [23] Collaborative and (2) Neural Filtering Collaborative (NCF) [24]. Filtering NCF simultaneously (NCF) [24]. NCF processes simultaneously item and user processes information item andusing user a dual information neural network. using a dual The neural two main network. NCF implementationsThe two main NCF are implementations (a) NCF [24] and are (b) (a) Neural NCF [24]Network and (b) Matrix Neural Factorization Network Matrix (NNMF) Factorization [25]. 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