Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey
Pith reviewed 2026-05-24 20:23 UTC · model grok-4.3
The pith
Deep learning addresses cold start in recommender systems via auxiliary features and latent representations, and candidate generation via dedicated networks, RNNs, autoencoders, and hybrids.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper claims that deep learning supplies practical solutions to cold start and candidate generation by incorporating additional data modalities for the former and specialized architectures for the latter, and it supplies a taxonomy that groups the surveyed techniques accordingly.
What carries the argument
A taxonomy that groups deep learning techniques by their handling of cold start (auxiliary features plus latent user/item representations) and candidate generation (separate networks, RNNs, autoencoders, hybrids).
If this is right
- Auxiliary features from images, text, or audio supply signals that reduce dependence on sparse user-item histories.
- Latent representation learning allows models to generalize to previously unseen users or items.
- Separate networks enable efficient filtering of large item sets before ranking.
- RNNs capture sequential user behavior patterns that improve candidate selection over static methods.
- Hybrid and autoencoder methods trade off accuracy against computational cost in generation pipelines.
Where Pith is reading between the lines
- The taxonomy could serve as a decision guide for selecting techniques when side information is available versus when only interaction logs exist.
- Integration with other recommender challenges such as scalability or fairness is left open by the current categorization.
- Empirical head-to-head comparisons across domains would be needed to quantify how often the mapped techniques actually outperform earlier non-deep baselines.
Load-bearing premise
The papers reviewed are representative of the field and the split between cold start and candidate generation captures the main technical distinctions without large omissions or overlaps.
What would settle it
A later survey that identifies many important deep learning methods omitted from the taxonomy or shows that traditional non-deep methods consistently outperform the surveyed approaches on cold start or candidate generation tasks.
Figures
read the original abstract
Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learning methods are beginning to get applied to recommender systems. Current survey papers on deep learning in recommender systems provide a historical overview and taxonomy of recommender systems based on type. Our paper addresses the gaps of providing a taxonomy of deep learning approaches to address recommender systems problems in the areas of cold start and candidate generation in recommender systems. We outline different challenges in recommender systems into those related to the recommendations themselves (include relevance, speed, accuracy and scalability), those related to the nature of the data (cold start problem, imbalance and sparsity) and candidate generation. We then provide a taxonomy of deep learning techniques to address these challenges. Deep learning techniques are mapped to the different challenges in recommender systems providing an overview of how deep learning techniques can be used to address them. We contribute a taxonomy of deep learning techniques to address the cold start and candidate generation problems in recommender systems. Cold Start is addressed through additional features (for audio, images, text) and by learning hidden user and item representations. Candidate generation has been addressed by separate networks, RNNs, autoencoders and hybrid methods. We also summarize the advantages and limitations of these techniques while outlining areas for future research.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript surveys deep learning methods in recommender systems, focusing on challenges of cold start and candidate generation. It partitions recommender system challenges into three categories: those related to recommendations (relevance, speed, accuracy, scalability), data nature (cold start, imbalance, sparsity), and candidate generation. It then provides a taxonomy mapping DL techniques to cold start (via additional features like audio/images/text and learning hidden representations) and candidate generation (via separate networks, RNNs, autoencoders, and hybrid methods), while summarizing advantages, limitations, and future directions.
Significance. A well-executed taxonomy could help structure the growing literature on DL for recsys by highlighting how specific techniques target these two challenges. The paper's contribution lies in its focused scope on cold start and candidate generation rather than a broad historical overview. However, without evidence of systematic literature selection, the significance is tempered by uncertainty about coverage and potential biases in the selected works.
major comments (2)
- [Abstract] Abstract: The abstract claims to address gaps in current survey papers by providing a taxonomy for cold start and candidate generation, but does not specify the search methodology, databases queried, or inclusion/exclusion criteria used to select the surveyed papers. This omission makes it impossible to assess whether the taxonomy is representative of the field.
- [Contribution paragraph (near end of abstract)] Contribution paragraph (near end of abstract): The taxonomy assumes a clean separation where cold start is addressed by side features and hidden representations, while candidate generation uses RNNs, autoencoders, etc. However, many autoencoder-based methods learn user/item embeddings that can simultaneously mitigate cold start issues, suggesting the categories may overlap substantially; the manuscript does not demonstrate non-overlap or justify the partitioning.
minor comments (2)
- The manuscript could benefit from a table summarizing the mapped techniques, their advantages, and limitations for quick reference.
- [Abstract] The phrasing 'would take one of the top places' is informal; consider 'rank among the top' for a more academic tone.
Simulated Author's Rebuttal
We thank the referee for the detailed and constructive feedback. We address the major comments point by point below.
read point-by-point responses
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Referee: [Abstract] Abstract: The abstract claims to address gaps in current survey papers by providing a taxonomy for cold start and candidate generation, but does not specify the search methodology, databases queried, or inclusion/exclusion criteria used to select the surveyed papers. This omission makes it impossible to assess whether the taxonomy is representative of the field.
Authors: We agree that the absence of explicit search methodology details limits the ability to evaluate representativeness. The manuscript is a focused survey highlighting key deep learning approaches rather than a systematic literature review. In the revision we will add a dedicated subsection describing the primary sources (major recsys and DL conferences/journals) and the rationale used to select representative papers on the two target challenges. revision: yes
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Referee: [Contribution paragraph (near end of abstract)] Contribution paragraph (near end of abstract): The taxonomy assumes a clean separation where cold start is addressed by side features and hidden representations, while candidate generation uses RNNs, autoencoders, etc. However, many autoencoder-based methods learn user/item embeddings that can simultaneously mitigate cold start issues, suggesting the categories may overlap substantially; the manuscript does not demonstrate non-overlap or justify the partitioning.
Authors: The referee correctly notes that techniques such as autoencoders can serve both purposes. Our taxonomy groups methods according to the primary challenge emphasized in the cited works, but we acknowledge the potential for overlap. We will revise the taxonomy section and contribution statement to explicitly discuss overlapping methods, provide concrete examples, and better justify the chosen partitioning while noting that some approaches address multiple challenges. revision: yes
Circularity Check
Survey taxonomy of DL methods for cold-start and candidate generation shows no circularity
full rationale
This is a literature survey paper. Its claims consist of statements about existing publications rather than any derivations, predictions, or fitted quantities. The taxonomy is presented as an organizational mapping of surveyed techniques to challenges; no equations, self-citation chains, or reductions of results to inputs by construction appear. The paper is self-contained as a descriptive review whose validity rests on the cited external literature.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
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[1]
They help the user in accelerating the process of search while helping businesses maximize sales
Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey Kiran R, Pradeep Kumar, Bharat Bhasker (efpm04013@iiml.ac.in, pradeepkumar@iiml.ac.in, bhasker@iimraipur.ac.in) IT and Systems Department Indian Institute of Management Lucknow Lucknow 226013, India Corresponding Author: Kiran Rama Address: No...
work page 2014
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[2]
and 60% of video clicks came from home page recommendations on YouTube (Davidson et al., 2010). We define Recommender systems based on several definitions in literature as “Systems that seek to predict the future preference of a set of items for a user either as a numeric rating for the item or as a list of recommendations or as a binary score indicating ...
work page 2010
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[3]
Advances in graphical processing unit hardware and decreasing costs have made GPUs accessible. With their multi-core nature, they enable highly parallel matrix computations that are at the core of Deep Learning. GPUs have been found to speed up the learning of a deep learning network by a factor of more than 50 (Schmidhuber, 2015). Consequently, Deep Lear...
work page 2015
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[4]
etc. The advancement of several frameworks like PyTorch from Facebook (Ketkar, 2017), MXNet from Apache (Chen et al.,
work page 2017
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[5]
The paper provides a historical course of development of techniques in the field
classified the techniques into methods that solely relied on deep learning and those that integrate deep learning with traditional recommender systems, further breaking them as loosely coupled and tightly coupled. The paper provides a historical course of development of techniques in the field. Another classification of deep learning for recommender syste...
work page 2017
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[6]
Making recommendations where there are no prior interactions available for an user or an item
is another survey paper on deep learning in recommender systems. None of these survey papers provide a taxonomy of deep learning methods based on the challenges of recommender systems that they address. The research objective of this paper is to address the literature gaps mapping different deep learning techniques against the recommendation system challe...
work page 2009
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[7]
highlight the cold start problem and classify it into user cold start and item cold start cases, referring to cases of insufficient examples of items and users respectively. Fig 3.1: Taxonomy of Deep Learning Methods to address Cold Start Problem Deep Learning Methods for Cold Start Audio Features Latent Audio Features CNN Sequential Audio Features RNN Im...
work page 2016
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[8]
CNNs have been applied to extract features from images (Oord et al., 2013)
and using audio content of the song to add some item based features alleviates the cold start problem. CNNs have been applied to extract features from images (Oord et al., 2013). The sequential property of audio has been used to build a LSTM-based recurrent neural network architecture in combination with audio based features from a convolutional networks ...
work page 2013
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[9]
show that collaborative filtering can be viewed as a sequence prediction problem and application of LSTMs is very competitive in addressing the cold start problem. Latent representations from the data are learnt in an unsupervised manner and the implicit relationships between items and users are learnt from both the content and the rating (Xiaopeng Li & S...
work page 2017
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[10]
with inputs as events from user history. The problem of returning hundreds of videos from millions was treated as an extreme multi-class problem and is solved using a deep neural network that learnt the user embeddings as a function of the user’s history and context. RNNs have been used for candidate generation for sequential data in Question-Answer Syste...
work page 2016
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[11]
combined word embeddings with L2 regularized large scale logistic regression to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Trivial business rule-based techniques and simple machine learning methods have been used combined with deep ...
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[12]
Retrieved from http://arxiv.org/abs/1502.04390 Davidson, J., Livingston, B., Sampath, D., Liebald, B., Liu, J., Nandy, P., … Lambert, M. (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems - RecSys ’10 (p. 293). https://doi.org/10.1145/1864708.1864770 Devooght, R., & Bersini, H. (2016). Colla...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.1145/1864708.1864770 2010
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[13]
https://doi.org/10.1109/MMUL.2011.34.van Portugal, I., Alencar, P., & Cowan, D. (2017). The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2017.12.020 Rumelhart, D. E., Hinton, G. E., & William, R. J. (1985). Learning internal representation by back- propagat...
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