A review on deep learning for recommender systems: challenges and remedies

By Zeynep Batmaz, Ali Yurekli, Alper Bilge, Cihan Kaleli. Artificial Intelligence Review, 2018

450 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1f8e9355f35da4854080b3376be5374e7fc07721

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Tat-seng Chua
7. Fei Sun
8. Bin Cui

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Fei Sun
7. Bin Cui
8. Shiwen Wu

In Management Science and Operations Research

1. Xiang Wang
2. Xiangnan He
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Aminu Da’u
7. Naomie Binti Salim
8. Fei Sun

Citations in the corpus

• A Survey on Deep Learning for Named Entity Recognition (2018, background): “Moreover, there is still a need for solutions on optimizing exponential growth of parameters when the size of data grows [179].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “[4] focus on most of the deep-learning techniques in recommender systemswhile ignoring GNN.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “For example, Zhang et al. [16] organized the discussions on recommendation methods into MLP based, autoencoder based, RNN based, attention based, etc. Similar surveys can also be found [17], [18].”
• A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “A review on deep learning for recommender systems: challenges and remedies [269] Deep Learning Methods on Recommender System: A Survey of State-of-theart [270] Deep Learning-Based Recommendation: Current Issues and Challenges [271] A Survey and Critique of Deep Learning on Recommender Systems [272]”
• A survey of the recent architectures of deep convolutional neural networks (2019, methodology): “ic feature maps [112]. 5 Applications of CNN CNN has been successfully applied to different ML related tasks, namely object detection, recognition, classification, regression, segmentation, etc [169]–[171]. However, CNN generally needs a large amount of data for learning. All of the aforementioned a”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “For example, Betru et al. (2017) presented a review of the state-of-the-art approaches on deep learning based RSs.”
• How Machine Learning will Transform Biomedicine (2020, unknown): “There already are many applications of this approach throughout science and society ranging from game playing (Silver et al., 2018) to product recommendations (Batmaz et al., 2019) to controlling self-driving cars (Bojarski et al., 2016).”
• Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (2019, background): “ecommendation.Inspired by the success of deep learning in computer vision and natural language processing [19], much efforts have been put for developing deep learning-based recommendation algorithms [3]. Besides the industrial applications proposed by [7, 31], various types of deep models have gain”
• Artificial intelligence in the creative industries: a review (2020, methodology): “] [60{62] [63,64] VAE [59] Analysis Ads/lm analysis [65] [65] [65] GP [66] Content retrieval [67{70] [71] [72] PM [73] Fake detection [74,75] [74] Blockchain [76] Recommendation [77] Deep belief net [78] Contrast enhancement [79] [80] Histogram [81]. Colorization [82{84] [85{88] Super-resolution [89”