Deep Cross Network (DCN) for Deep Learning Recommendation Systems

For last 2 weeks, I have researched and worked on developing a Video Recommendation System. For many years, Content-based and Collaborative Filtering approaches have been heavily used in Recommendation Systems. Content-based system bases on similarity among items’ characteristics (e.g. cosine similarity) ; and Collaborative Filtering system bases on user-item interactions (e.g. Alternating Least Squares). These 2 systems have gained success in industry; however, based on my understanding, these 2 approaches are limited by large spare matrix and non-genearlization. Hence, in this article, I introduce and explain Deep Cross Network for Recommendation Systems presented in the following paper: Deep & Cross Network for Ad Click Predictions.

Motivation

Click-Through Rate (CTR) is a large-scale problem in the advertising industry that advertisers pay publishers to examine and displays that are clicked frequently to improve product/brand recognition. Examination and prediction of CTR of ads recommend advertisers and publishers how to improve the ads. Hence, CTR is also considered as a recommendation problem.

Now, let’s dig deep into Deep Cross Network and see how simple and interesting it is.

The rest of this article is structured into parts:

• Embedding & Stacking Layer
• Cross Network
• Deep Network
• Combination Output Layer
• LogLoss
• Implementation & Training
• Model Analysis: Advantages

Embedding & Stacking Layer

In the exploding era of Internet and Social Media, the size and dimensionality of data generated by humans increase dramatically. To avoid extensive task-specific feature engineering (accounts 75% of time of AI projects), Embedding layer is used after Input layer in Fig. 1 to convert sparse features (e.g. categorical features )into low-dimensional features. Embedding layer was first invented in Natural Language Processing to convert tokenized words into dimension-fixed and dense metrics, aka Word Embeddings [5]. This method is to avoid the high-dimensional and sparse features generated by CountVectorizer or TF-IDF [2] that Embeddings lead to less computation and feature engineering on text. Then, processed continuous features (e.g. normalized) and embeddings of categorical features are stacked together.

#deep-cross-network #click-through-rate #embedding #deep learning

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Deep Cross Network (DCN) for Deep Learning Recommendation Systems

For last 2 weeks, I have researched and worked on developing a Video Recommendation System. For many years, Content-based and Collaborative Filtering approaches have been heavily used in Recommendation Systems. Content-based system bases on similarity among items’ characteristics (e.g. cosine similarity) ; and Collaborative Filtering system bases on user-item interactions (e.g. Alternating Least Squares). These 2 systems have gained success in industry; however, based on my understanding, these 2 approaches are limited by large spare matrix and non-genearlization. Hence, in this article, I introduce and explain Deep Cross Network for Recommendation Systems presented in the following paper: Deep & Cross Network for Ad Click Predictions.

Motivation

Click-Through Rate (CTR) is a large-scale problem in the advertising industry that advertisers pay publishers to examine and displays that are clicked frequently to improve product/brand recognition. Examination and prediction of CTR of ads recommend advertisers and publishers how to improve the ads. Hence, CTR is also considered as a recommendation problem.

Now, let’s dig deep into Deep Cross Network and see how simple and interesting it is.

The rest of this article is structured into parts:

• Embedding & Stacking Layer
• Cross Network
• Deep Network
• Combination Output Layer
• LogLoss
• Implementation & Training
• Model Analysis: Advantages

Embedding & Stacking Layer

In the exploding era of Internet and Social Media, the size and dimensionality of data generated by humans increase dramatically. To avoid extensive task-specific feature engineering (accounts 75% of time of AI projects), Embedding layer is used after Input layer in Fig. 1 to convert sparse features (e.g. categorical features )into low-dimensional features. Embedding layer was first invented in Natural Language Processing to convert tokenized words into dimension-fixed and dense metrics, aka Word Embeddings [5]. This method is to avoid the high-dimensional and sparse features generated by CountVectorizer or TF-IDF [2] that Embeddings lead to less computation and feature engineering on text. Then, processed continuous features (e.g. normalized) and embeddings of categorical features are stacked together.

#deep-cross-network #click-through-rate #embedding #deep learning

Deep learning on graphs: successes, challenges, and next steps

TL;DR This is the first in a [series of posts] where I will discuss the evolution and future trends in the field of deep learning on graphs.

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the ML community and even to the popular science press (with Quanta Magazine running a series of excellent articles on geometric deep learning for the study of manifolds, drug discovery, and protein science).

Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. Since the amount of graph-structured data produced in some of these fields nowadays is enormous (prominent examples being social networks like Twitter and Facebook), it is very tempting to try to apply deep learning techniques that have been remarkably successful in other data-rich settings.

There are multiple flavours to graph learning problems that are largely application-dependent. One dichotomy is between node-wise and graph-wise problems, where in the former one tries to predict properties of individual nodes in the graph (e.g. identify malicious users in a social network), while in the latter one tries to make a prediction about the entire graph (e.g. predict solubility of a molecule). Furthermore, like in traditional ML problems, we can distinguish between supervised and unsupervised (or self-supervised) settings, as well as transductive and inductive problems.

Similarly to convolutional neural networks used in image analysis and computer vision, the key to efficient learning on graphs is designing local operations with shared weights that do message passing [5] between every node and its neighbours. A major difference compared to classical deep neural networks dealing with grid-structured data is that on graphs such operations are permutation-invariant, i.e. independent of the order of neighbour nodes, as there is usually no canonical way of ordering them.

Despite their promise and a series of success stories of graph representation learning (among which I can selfishly list the [acquisition by Twitter] of the graph-based fake news detection startup Fabula AI I have founded together with my students), we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In the following, I will try to outline my views on the possible reasons and how the field could progress in the next few years.

**Standardised benchmarks **like ImageNet were surely one of the key success factors of deep learning in computer vision, with some [6] even arguing that data was more important than algorithms for the deep learning revolution. We have nothing similar to ImageNet in scale and complexity in the graph learning community yet. The [Open Graph Benchmark] launched in 2019 is perhaps the first attempt toward this goal trying to introduce challenging graph learning tasks on interesting real-world graph-structured datasets. One of the hurdles is that tech companies producing diverse and rich graphs from their users’ activity are reluctant to share these data due to concerns over privacy laws such as GDPR. A notable exception is Twitter that made a dataset of 160 million tweets with corresponding user engagement graphs available to the research community under certain privacy-preserving restrictions as part of the [RecSys Challenge]. I hope that many companies will follow suit in the future.

**Software libraries **available in the public domain played a paramount role in “democratising” deep learning and making it a popular tool. If until recently, graph learning implementations were primarily a collection of poorly written and scarcely tested code, nowadays there are libraries such as [PyTorch Geometric] or [Deep Graph Library (DGL)] that are professionally written and maintained with the help of industry sponsorship. It is not uncommon to see an implementation of a new graph deep learning architecture weeks after it appears on arxiv.

Scalability is one of the key factors limiting industrial applications that often need to deal with very large graphs (think of Twitter social network with hundreds of millions of nodes and billions of edges) and low latency constraints. The academic research community has until recently almost ignored this aspect, with many models described in the literature completely inadequate for large-scale settings. Furthermore, graphics hardware (GPU), whose happy marriage with classical deep learning architectures was one of the primary forces driving their mutual success, is not necessarily the best fit for graph-structured data. In the long run, we might need specialised hardware for graphs [7].

**Dynamic graphs **are another aspect that is scarcely addressed in the literature. While graphs are a common way of modelling complex systems, such an abstraction is often too simplistic as real-world systems are dynamic and evolve in time. Sometimes it is the temporal behaviour that provides crucial insights about the system. Despite some recent progress, designing graph neural network models capable of efficiently dealing with continuous-time graphs represented as a stream of node- or edge-wise events is still an open research question.

#deep-learning #representation-learning #network-science #graph-neural-networks #geometric-deep-learning #deep learning

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time

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Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

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Deep Learning Explained in Layman's Terms

In this post, you will get to learn deep learning through a simple explanation (layman terms) and examples.

Deep learning is part or subset of machine learning and not something that is different than machine learning. Many of us, when starting to learn machine learning, try and look for the answers to the question, “What is the difference between machine learning and deep learning?” Well, both machine learning and deep learning are about learning from past experience (data) and make predictions on future data.

Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural networks (a mathematical model mimicking the human brain). Here is the diagram representing the similarity and dissimilarity between machine learning and deep learning at a very high level.

#machine learning #artificial intelligence #deep learning #neural networks #deep neural networks #deep learning basics