DropoutNet: Addressing Cold Start in Recommender Systems

By Maksims Volkovs, Guangwei Yu, Tomi Poutanen. Neural Information Processing Systems, 2017

372 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ba21486b2bf93174ad75b15846d9faea9b9e8e07

How researchers see this paper

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

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Fuli Feng
5. Yin-wei Wei
6. Liqiang Nie
7. Junliang Yu
8. Chen Gao

Citations in the corpus

• Self-supervised Graph Learning for Recommendation (2020, background): “pout processes �′and �′′, this approach encourages partial information to recover the instance-wise embeddings, and learn not to heavily rely on certain information, so as to improve model robustness [41]. Note that the stochastic selections are easy to adapt to contentbased recommendation models. N”
• Neural Cognitive Diagnosis for Intelligent Education Systems (2019, background): “n-linear interactions. Although the capability of neural networks to approximate continuous functions has been proved in many domains, such as natural language processing [12] and recommender systems [13], it is still highly nontrivial to adapt to cognitive diagnosis due to the following domain chal”
• Sampling-bias-corrected neural modeling for large corpus item recommendations (2019, background): “, [21, 34]) addresses the aforementioned challenges in learning from a large corpus.”
• Deep Learning Based Recommender System (2017, unknown): “Others Cold-start [154, 156, 170, 171] Multitask [5, 73, 87, 174, 187] Explainability [87, 126]”
• Contrastive Learning for Cold-Start Recommendation (2021, methodology): “In this research line, extensive studies have been conducted, which can be roughly categorized into two types, robustness-based [8, 34, 38, 43] and constraint-based [2, 3, 26, 37, 54].”
• Self-supervised Learning for Large-scale Item Recommendations (2020, background): “• Feature Dropout (FD) [35]: Backbone model with random feature dropout on the item tower in the supervised learning task.”
• Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation (2020, methodology): “(3) Cold-startmethods, i.e., DropoutNet, MeteEmb and MeLU.”
• A review on deep learning for recommender systems: challenges and remedies (2018, unknown): “…et al. (2017) Shankar et al. (2017) Pana et al. (2017) He et al. (2017) 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.…”
• Robust Federated Recommendation System (2020, background): “Recommendation systems (RS), which are machine learning models that try to predict the user (client) preferences, are increasingly being deployed commercially [22; 4; 26; 37; 32; 35].”