Wide &amp; Deep Learning for Recommender Systems

Cheng, Heng-Tze; Koç, L. Yasemin; Harmsen, Jeremiah; Shaked, Tal; Chandra, Tushar; Aradhye, Hrishi; Anderson, Glen; Corrado, Greg S.; Chai, Wei Koong; Ispir, Mustafa; Anil, Rohan; Haque, Zakaria; Hong, Lichan; Jain, Vihan; Liu, Xiaobing; Shah, Hemal

doi:10.48550/arxiv.1606.07792

Cited by 83 publications

(17 citation statements)

References 5 publications

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“…Indeed, CoCounts memorizes frequent pairs of (query, target) product, while Standalone Meta-Prod2Vec helps to generalize on unseen ones. These results are mirrored by similar findings covered in [6] and motivate the newly introduced approach of Wide and Deep learning.…”

Section: Improvements On Cold-startsupporting

confidence: 82%

Meta-Prod2Vec

Vasile

Смирнова

Conneau³

2016

Proceedings of the 10th ACM Conference on Recommender Systems

141

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We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset.

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Section: Improvements On Cold-startsupporting

confidence: 82%

Meta-Prod2Vec

Vasile

Смирнова

Conneau³

2016

Proceedings of the 10th ACM Conference on Recommender Systems

141

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“…We hence recommend to use KRR for first explorative runs and switch to NNs for final production runs of excited-state dynamics simulations. Future applications might use the concept of wide and deep learning [70] in the sense that different ML models can be applied within one applications to combine their distinct benefits.…”

Section: Discussionmentioning

confidence: 99%

Neural networks and kernel ridge regression for excited states dynamics of CH₂NH 2+ : From single-state to multi-state representations and multi-property machine learning models

Westermayr

Faber

Christensen

et al. 2020

Mach. Learn.: Sci. Technol.

155

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Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since the applicability of these simulation techniques is limited by the costs of the underlying electronic structure calculations, we develop and assess different machine learning models for this task. The machine learning models are trained on ab initio calculations for excited electronic states, using the methylenimmonium cation (CH 2 NH + 2 ) as a model system. For the prediction of excited-state properties, multiple outputs are desirable, which is straightforward with neural networks but less explored with kernel ridge regression. We overcome this challenge for kernel ridge regression in the case of energy predictions by encoding the electronic states explicitly in the inputs, in addition to the molecular representation. We adopt this strategy also for our neural networks for comparison. Such a state encoding enables not only kernel ridge regression with multiple outputs but leads also to more accurate machine learning models for state-specific properties. An important goal for excited-state machine learning models is their use in dynamics simulations, which needs not only state-specific information but also couplings, i.e., properties involving pairs of states. Accordingly, we investigate the performance of different models for such coupling elements. Furthermore, we explore how combining all properties in a single neural network affects the accuracy. As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.

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“…More recently, Google publicized their Wide & Deep learning approach for App recommendation [4]. The deep component similarly uses a MLP on feature embeddings, which has been reported to have strong generalization ability.…”

Section: Related Workmentioning

confidence: 99%

Neural Collaborative Filtering

Liao

Zhang

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

4,409

3,398

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In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -collaborative filtering -on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural networkbased Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

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Wide & Deep Learning for Recommender Systems

Cited by 83 publications

References 5 publications

Meta-Prod2Vec

Meta-Prod2Vec

Neural networks and kernel ridge regression for excited states dynamics of CH₂NH 2+ : From single-state to multi-state representations and multi-property machine learning models

Neural Collaborative Filtering

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Wide & Deep Learning for Recommender Systems

Cited by 83 publications

References 5 publications

Meta-Prod2Vec

Meta-Prod2Vec

Neural networks and kernel ridge regression for excited states dynamics of CH2NH 2+ : From single-state to multi-state representations and multi-property machine learning models

Neural Collaborative Filtering

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Neural networks and kernel ridge regression for excited states dynamics of CH₂NH 2+ : From single-state to multi-state representations and multi-property machine learning models