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In this "info-plosion" era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activities and e-commerce. Several techniques have been widely applied for recommendation systems, but the cold-start and sparsity problems remain a major challenge. The cold-start problem occurs when generating recommendations for new users and items without sufficient information. Sparsity refers to the problem of having a large amount of users and items but with few transactions or interactions. In this article, a novel cross-domain recommendation model, Cross-Domain Evolution Learning Recommendation (abbreviated as CD-ELR), is developed to communicate the information from different domains in order to tackle the cold-start and sparsity issues by integrating matrix factorization and recurrent neural network. We introduce an evolutionary concept to describe the preference variation of users over time. Furthermore, several optimization methods are developed for combining the domain features for precision recommendation. Experimental results show that CD-ELR outperforms existing state-of-the-art recommendation baselines. Finally, we conduct experiments on several real-world datasets to demonstrate the practicability of the proposed CD-ELR. [ABSTRACT FROM AUTHOR]

The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past fewyears, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. To obtain a better understanding of the actual progress, we have compared recent results in the area of neural recommendation approaches based on collaborative filtering against a consistent set of existing simple baselines. The worrying outcome of the analysis of these recent works--all were published at prestigious scientific conferences between 2015 and 2018--is that 11 of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristic or linear models. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation. [ABSTRACT FROM AUTHOR]

Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users' feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users' feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users' preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging comparedwith that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases) with the assistance of the auxiliary feedback (e.g., examinations). In this survey, we provide an overview of the representative HOCCF methods fromthe perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorizationbased methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions. [ABSTRACT FROM AUTHOR]

Recommendation systems play a vital role to keep users engaged with personalized contents in modern online platforms. Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it for recommendation. However, existing studies have largely focused on exploring complex deeplearning architectures for recommendation task, while typically applying the negative sampling strategy for model learning. Despite effectiveness, we argue that these methods suffer from two important limitations: (1) the methods with complex network structures have a substantial number of parameters, and require expensive computations even with a sampling-based learning strategy; (2) the negative sampling strategy is not robust, making sampling-based methods difficult to achieve the optimal performance in practical applications. In this work, we propose to learn neural recommendation models from the whole training data without sampling. However, such a non-sampling strategy poses strong challenges to learning efficiency. To address this, we derive three new optimization methods through rigorous mathematical reasoning, which can efficiently learn model parameters from the whole data (including all missing data) with a rather low time complexity. Moreover, based on a simple Neural Matrix Factorization architecture, we present a general framework named ENMF, short for Efficient Neural Matrix Factorization. Extensive experiments on three real-world public datasets indicate that the proposed ENMF framework consistently and significantly outperforms the state-of-the-art methods on the Top-K recommendation task. Remarkably, ENMF also shows significant advantages in training efficiency, which makes it more applicable to real-world large-scale systems. [ABSTRACT FROM AUTHOR]

In this article, we study a multi-step interactive recommendation problem for explicit-feedback recommender systems. Different from the existing works, we propose a novel user-specific deep reinforcement learning approach to the problem. Specifically, we first formulate the problem of interactive recommendation for each target user as a Markov decision process (MDP). We then derive a multi-MDP reinforcement learning task for all involved users. To model the possible relationships (including similarities and differences) between different users' MDPs, we construct user-specific latent states by using matrix factorization. After that, we propose a user-specific deepQ-learning (UDQN) method to estimate optimal policies based on the constructed user-specific latent states. Furthermore, we propose Biased UDQN (BUDQN) to explicitly model user-specific information by employing an additional bias parameter when estimating the Q-values for different users. Finally, we validate the effectiveness of our approach by comprehensive experimental results and analysis. [ABSTRACT FROM AUTHOR]