Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
TLDR
The recent advances made in the field of recommendation using various variants of deep learning technology are covered and whether deep learning has shown any significant improvement over the conventional systems for recommendation is discussed.Abstract:
With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.read more
Citations
More filters
Posted Content
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
TL;DR: This survey illustrates the concept of sequential recommendation, proposes a categorization of existing algorithms in terms of three types of behavioral sequence, and summarizes the key factors affecting the performance of DL-based models and conducts corresponding evaluations to demonstrate the effects of these factors.
Journal ArticleDOI
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
TL;DR: In the field of sequential recommendation, deep learning--(DL) based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and f...
Journal ArticleDOI
A Deep Reinforcement Learning Approach to Proactive Content Pushing and Recommendation for Mobile Users
Dong Liu,Chenyang Yang +1 more
TL;DR: A joint content pushing and recommendation problem that maximizes the net profit of a mobile network operator is formulated and a reinforcement learning (RL) framework is established to resolve the problem.
Journal ArticleDOI
Lowering the latency of data processing pipelines through FPGA based hardware acceleration
TL;DR: This work focuses on accelerating the scoring function implemented as a decision tree ensemble, a common approach to scoring and classification in search systems, on an FPGA to increase the number of entries that can be scored per unit of time.
Journal ArticleDOI
Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations
TL;DR: A recurrent neural network with an attention mechanism is built, capable of obtaining users’ preferences in the current session and consequently making recommendations, which outperforms the current state-of-the-art short-term music recommendation systems on one real-world dataset.
References
More filters
Proceedings ArticleDOI
Neural Collaborative Filtering
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Proceedings ArticleDOI
Collaborative Deep Learning for Recommender Systems
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.
Journal ArticleDOI
Recommender system application developments
TL;DR: This paper reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories, and summarizes the related recommendation techniques used in each category.
Proceedings Article
Deep content-based music recommendation
TL;DR: This paper proposes to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data, and shows that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.
Posted Content
Graph Convolutional Matrix Completion
TL;DR: A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.
Related Papers (5)
A survey of recommendation techniques based on offline data processing
Yongli Ren,Gang Li,Wanlei Zhou +2 more