Deep Learning based Recommender System: A Survey and New Perspectives (1707.07435v7)

Abstract: With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.

• The paper presents a comprehensive taxonomy of DL-based recommender systems, categorizing models like MLP, AE, CNN, and RNN for improved personalization.
• It demonstrates how deep learning models capture nonlinear user-item interactions, outperforming traditional methods in feature extraction and scalability.
• The survey identifies future research trends including multimodal integration, explainable recommendations, and scalable, real-time system adaptations.

Deep Learning Based Recommender Systems: An Expert Overview

The paper by Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay presents a comprehensive survey on deep learning (DL) based recommender systems. Given the exponential growth in the volume of online information, recommender systems have become indispensable for personalizing user experiences. This survey offers an extensive review of DL techniques applied to recommendation models, providing a taxonomy, summarizing the state-of-the-art, and discussing future trends and challenges.

Overview of Recommender Systems and Deep Learning

Recommender systems have traditionally been categorized into collaborative filtering (CF), content-based, and hybrid systems. Collaborative filtering leverages user-item interactions, content-based systems utilize item attributes, and hybrid models integrate multiple data sources.

Deep learning, characterized by its ability to learn feature representations, has demonstrated remarkable success in various domains, including computer vision and natural language processing. Its application to recommender systems has shown promise in capturing non-linear user-item relationships, thus enhancing recommendation quality.

Categories of Deep Learning Based Recommendation Models

The paper classifies deep learning-based recommendation models into several categories based on the employed techniques:

1. Multilayer Perceptron (MLP): MLPs, due to their universal approximation capabilities, have been used as nonlinear extensions to traditional recommendation models. Significant works include Neural Collaborative Filtering (NCF) and Deep Factorization Machine (DeepFM).
2. Autoencoder (AE): Autoencoder-based models, such as AutoRec and Collaborative Denoising Autoencoder (CDAE), employ deep architectures to learn latent feature representations, particularly useful for collaborative filtering and hybrid recommenders.
3. Convolutional Neural Networks (CNNs): CNNs are effective for extracting features from unstructured data, such as images and text. Models like VBPR (Visual Bayesian Personalized Ranking) and DeepCoNN (Deep Cooperative Neural Networks) integrate visual/textual features into the recommendation process.
4. Recurrent Neural Networks (RNNs): RNNs, including LSTM and GRU variants, are apt for modeling temporal dynamics and sequential behaviors. GRU4Rec and Recurrent Recommender Network (RRN) are notable examples in session-based and sequential recommendation tasks.
5. Restricted Boltzmann Machines (RBM): RBM-based models capture latent user-item preferences through unsupervised learning. Conditional RBM has been extended for integrating implicit feedback.
6. Neural Attention Models: Attention mechanisms enhance recommendation accuracy by focusing on informative parts of the input. Co-attention models, like AttRec, highlight the relevance of different sections of input data for better recommendations.
7. Neural AutoRegressive Models (NADE): Transferable from RBMs, NADE models like CF-NADE offer tractable distribution estimation for collaborative filtering, improving performance with implicit feedback integration.
8. Deep Reinforcement Learning (DRL): DRL frameworks adapt dynamically to users' real-time interactions, showing promise in sequential and long-term engagement tasks.
9. Adversarial Networks: Generative Adversarial Networks (GANs) enhance recommender systems by simulating realistic user-item interactions. IRGAN and adversarial personalized ranking models illustrate successful applications of GANs.

Practical and Theoretical Implications

The adoption of deep learning for recommender systems brings several practical and theoretical implications:

• Nonlinear Interaction Modeling: Deep learning models can capture complex user-item interaction patterns, surpassing the capabilities of traditional linear models.
• Feature Representation Learning: Automatic feature engineering through deep models reduces manual effort and integrates heterogeneous data sources effectively.
• Sequential and Contextual Modeling: RNNs and attention mechanisms excel in modeling temporal and contextual dynamics, aiding in personalized and timely recommendations.
• Scalability and Real-time Adaptation: With growing computational power, especially GPU-based acceleration, deep learning models offer scalable solutions for vast datasets, although efficient training and inference remain challenging.

Future Directions

The paper identifies several open issues and future research directions:

1. Joint Representation Learning: Integrating multimodal data (text, images, audio) in a unified framework.
2. Explainable Recommendation: Enhancing the interpretability of deep learning models to provide transparent recommendations.
3. Deep Architectures: Exploring deeper neural networks to improve recommendation accuracy.
4. Machine Reasoning: Adapting machine reasoning techniques from NLP and computer vision domains to recommendation tasks.
5. Cross-domain Recommendation: Utilizing transfer learning for recommendations across diverse user activities and platforms.
6. Multi-task Learning: Leveraging the shared features among related tasks to improve recommendation systems.
7. Scalability: Developing methods to handle large-scale data efficiently, focusing on incremental learning and model compression.
8. Standardized Evaluation: Establishing unified benchmarks and evaluation protocols to foster consistent and reproducible research outcomes.

Conclusion

This survey meticulously analyzes the integration of deep learning techniques into recommender systems, illustrating the advantages and obstacles while providing insightful directions for future research. The rapid evolution of deep learning continues to revolutionize the landscape of personalized recommendations, offering new paradigms and solutions to longstanding challenges in the domain.