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:10.1145/2988450.2988454

Cited by 2,811 publications

(1,374 citation statements)

References 7 publications

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“…The deep network is composed of two primary parts, and while not exactly the same, is inspired by recent research into Wide-and-Deep models where sparse linear inputs are combined with deep learning to provide a more accurate model (Cheng et al 2016). The analogous 'wide' input is a one-hot encoded input of the CPC codes for a patent and a one-hot encoded input of reference publication numbers.…”

Section: Machine Learning Methodologiesmentioning

confidence: 99%

Automated patent landscaping

Abood

Feltenberger

2018

Artif Intell Law

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Patent landscaping is the process of finding patents related to a particular topic. It is important for companies, investors, governments, and academics seeking to gauge innovation and assess risk. However, there is no broadly recognized best approach to landscaping. Frequently, patent landscaping is a bespoke human-driven process that relies heavily on complex queries over bibliographic patent databases. In this paper, we present Automated Patent Landscaping, an approach that jointly leverages human domain expertise, heuristics based on patent metadata, and machine learning to generate high-quality patent landscapes with minimal effort. In particular, this paper describes a flexible automated methodology to construct a patent landscape for a topic based on an initial seed set of patents. This approach takes human-selected seed patents that are representative of a topic, such as operating systems, and uses structure inherent in patent data such as references and class codes to ''expand'' the seed set to a set of ''probably-related'' patents and anti-seed ''probably-unrelated'' patents. The expanded set of patents is then pruned with a semi-supervised machine learning model trained on seed and anti-seed patents. This removes patents from the expanded set that are unrelated to the topic and ensures a comprehensive and accurate landscape.

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Section: Machine Learning Methodologiesmentioning

confidence: 99%

Automated patent landscaping

Abood

Feltenberger

2018

Artif Intell Law

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“…Machine learning methods are often trained in so-called batch modes. Nevertheless, many applications in the field of autonomous robotics or driving are trained on the basis of continuously arriving training data [15]. Thus, incremental learning facilitates learning from streaming data and hence is exposed to continuous model adaptation [15].…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, many applications in the field of autonomous robotics or driving are trained on the basis of continuously arriving training data [15]. Thus, incremental learning facilitates learning from streaming data and hence is exposed to continuous model adaptation [15]. Especially handling non-stationary data assumes key importance in applications like voice and face recognition due to dynamically evolving patterns.…”

Section: Related Workmentioning

confidence: 99%

Improving System Reliability Assessment of Safety-Critical Systems using Machine Learning Optimization Techniques

Alagoz¹,

Hoiss²,

German³

2018

Adv. sci. technol. eng. syst. j.

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“…This model is used for CTR prediction and tested commercial data. This system takes the advantage of the Wide & Deep model proposed by Google [29] which joint trained wide linear models and deep neural networks to overcome the sparsity of user-item interactions matrix.…”

Section: Deep Collaborative Filtering Recommendationmentioning

confidence: 99%

Deep Learning-Based Recommendation: Current Issues and Challenges

Fakhfakh¹,

Ammar²,

Amar³

2017

ijacsa

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Abstract-Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

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Wide & Deep Learning for Recommender Systems

Cited by 2,811 publications

References 7 publications

Automated patent landscaping

Automated patent landscaping

Improving System Reliability Assessment of Safety-Critical Systems using Machine Learning Optimization Techniques

Deep Learning-Based Recommendation: Current Issues and Challenges

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