Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.

Recommender System (RS) is an important online application that affects billions of users every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i.e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction. There has not been much research on the fusion model while it has great impact on the final recommendation as the last crucial process of the ranking. To optimize long-term user satisfaction rather than obtain instant returns greedily, we formulate MTF task as Markov Decision Process (MDP) within a recommendation session and propose a Batch Reinforcement Learning (RL) based Multi-Task Fusion framework (BatchRL-MTF) that includes a Batch RL framework and an online exploration. The former exploits Batch RL to learn an optimal recommendation policy from the fixed batch data offline for long-term user satisfaction, while the latter explores potential high-value actions online to break through the local optimal dilemma. With a comprehensive investigation on user behaviors, we model the user satisfaction reward with subtle heuristics from two aspects of user stickiness and user activeness. Finally, we conduct extensive experiments on a billion-sample level real-world dataset to show the effectiveness of our model. We propose a conservative offline policy estimator (Conservative-OPEstimator) to test our model offline. Furthermore, we take online experiments in a real recommendation environment to compare performance of different models. As one of few Batch RL researches applied in MTF task successfully, our model has also been deployed on a large-scale industrial short video platform, serving hundreds of millions of users.

Google has sued Sonos, alleging that its new voice assistant violates seven patents related to its own Google Assistant technology, CNET has reported. It's the latest salvo in a long-running smart speaker battle between the companies, with each suing and countersuing the other following a period when they worked together. "[Sonos has] started an aggressive and misleading campaign against our products, at the expense of our shared customers," a Google spokesperson said in a statement. Sonos' Voice Control assistant arrived in June, letting users give commands with the phrase "Hey Sonos," much like Amazon's Alexa or Google Assistant. In the complaint, Google said it "worked for years with Sonos engineers on the implementation of voice recognition and voice-activated devices control in Sonos products... even providing its Google Assistant software to Sonos for many years."

Speech AI can assist human agents in contact centers, power virtual assistants and digital avatars, generate live captioning in video conferencing, and much more. Under the hood, these voice-based technologies orchestrate a network of automatic speech recognition (ASR) and text-to-speech (TTS) pipelines to deliver intelligent, real-time responses. Building these real-time speech AI applications from scratch is no easy task. From setting up GPU-optimized development environments to deploying speech AI inferences using customized large transformer-based language models in under 300ms, speech AI pipelines require dedicated time, expertise, and investment. In this post, we walk through how you can simplify the speech AI development process by using NVIDIA Riva to run GPU-optimized applications.

Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.

A recommender system is a system that helps users filter irrelevant information and create user interest models based on their historical records. With the continuous development of Internet information, recommendation systems have received widespread attention in the industry. In this era of ubiquitous data and information, how to obtain and analyze these data has become the research topic of many people. In view of this situation, this paper makes some brief overviews of machine learning-related recommendation systems. By analyzing some technologies and ideas used by machine learning in recommender systems, let more people understand what is Big data and what is machine learning. The most important point is to let everyone understand the profound impact of machine learning on our daily life.

Artificial Intelligence is becoming more and more popular as more and more software is being created for our convenience. AI technology will soon become more common, and more and more intelligent software solutions will be created to make our lives easier. In this article, we will be covering 10 of the best Artificial Intelligence Software and giving a brief review of the software. Artificial intelligence software is becoming more and more popular, with Microsoft's Cortana voice assistant reaching a user base of over 100 million users. Cortana is a virtual assistant that can help you navigate your day-to-day life.

AI is the future of business, and it's already here. It has a great business impact that every entrepreneur should embrace. AI is a major focus of digital marketing right now, which means that businesses need to be on top of their game when it comes to virtual assistants. They're an AI-powered tool that can help with tasks like scheduling meetings, managing your inbox, analyzing data and more. Think of them like a digital assistant in your pocket -- except this one can do so much more than schedule your events or manage your inbox.

Artificial intelligence (AI) is redefining the travel industry and assisting businesses in achieving their objectives. AI in the travel industry is continuing to provide cutting-edge services to travelers. Tour and travel companies are incorporating artificial intelligence to improve the traveler experience. AI-enabled apps assist users or travelers in automatically adjusting the prices of flights or rooms based on their needs. Travelers today rely on technology to keep them moving in their pursuit of preferences worldwide.

Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space by leveraging the id's frequency statistics of the target dataset. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner. It is also scaled to multiple GPUs in combination with the widely used hybrid parallel training approaches. Evaluating our prototype system shows that we can keep only 1.5% of the embedding parameters in the GPU to obtain a decent end-to-end training speed.