Personalized Deep Learning for Tag Recommendation

By Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Drumond, Lars Schmidt-Thieme. 2017

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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. Yi Tay
2. Shuai Zhang
3. Aixin Sun
4. Lina Yao
5. Jiliang Tang
6. Long Xia
7. Zhuoye Ding
8. Ming Chen

In Computer Vision and Pattern Recognition

1. Yi Tay
2. Long Xia
3. Shuai Zhang
4. Aixin Sun
5. Jiliang Tang
6. Zhuoye Ding
7. Lina Yao
8. Ming Chen

In Information Systems

1. Yi Tay
2. Xiangyu Zhao
3. Aixin Sun
4. Lina Yao
5. Shuai Zhang
6. Jiliang Tang
7. Dawei Yin
8. Long Xia

Citations in the corpus

• AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020, background): “Driven by the recent advances in deep learning, there have been increasing interests in developing deep learning based recommender systems (DLRSs) [20, 30, 34].”
• Deep Reinforcement Learning for List-wise Recommendations (2017, methodology): “For instance, Nguyen et al.[17] proposed a personalized tag recommender system based on CNN.”
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018, methodology): “apture the non-linear and non-trivial user-item relationships, and enables the codification of more complex abstractions as data representations in the higher layers [32]. For instance, Nguyen et al. [20] proposed a personalized tag recommender system based on CNN. It utilizes constitutional and max”
• Deep Learning Based Recommender System (2017, background): “der [34, 88, 89, 114, 116, 125, 136, 137, 140, 159, 177, 187, 207], [4, 10, 32, 94, 150, 151, 158, 170, 171, 188, 196, 208, 209] CNNs [25, 49, 50, 75, 76, 98, 105, 127, 130, 153, 165, 172, 202, 206], [6, 44, 51, 83, 110, 126, 143, 148, 169, 190, 191] RNNs [5, 28, 35, 56, 57, 73, 78, 90, 117, 132, 13”
• Deep reinforcement learning for page-wise recommendations (2018, methodology): “ capture the non-linear and non-trivial user-item relationships, and enables the codification of more complex abstractions as data representations in the higher layers[37]. For instance, Nguyen et al.[23] proposed a personalized tag recommender system based on CNN. It utilizes constitutional and max”
• A review on deep learning for recommender systems: challenges and remedies (2018, unknown): “…Zhang et al. (2017c) Wang et al. (2017) Wang et al. (2016) Xu et al. (2017a) Volkovs et al. (2017) Rassweiler Filho et al. (2017) Vall et al. (2017) Nguyen et al. (2017) Hassan (2017) Seo et al. (2017b) Nedelec et al. (2017) Peska and Trojanova (2017) Deldjoo et al. (2017) Zhang et al.…”