From recommending movies or restaurants to coordinating fashion accessories and highlighting blog posts and news articles, recommender systems are an important application of machine learning, surfacing new discoveries and helping users find what they love. At Google, we have spent the last several years exploring new deep learning techniques to provide better recommendations through multi-task learning, reinforcement learning, better user representations and fairness objectives. These and other advancements have allowed us to greatly improve our recommendations. Today, we're excited to introduce TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy. Built with TensorFlow 2.x, TFRS makes it possible to: TFRS is based on TensorFlow 2.x and Keras, making it instantly familiar and user-friendly.

Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes" in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.

Amazon is making Alexa smarter with natural turn-taking, having conversations with multiple people, natural language understanding, and the ability to be taught by customers. The first target is the smart home, but Alexa for Business is also likely to follow. Also: When is Prime Day 2020? The Alexa overhaul and artificial intelligence improvements were outlined as Amazon launched its latest batch of Echo devices. Amazon's new Echo devices are evolving to be more smart home edge computing devices.

As some of you might know, I'm a runner. On occasion I review sports watches, and outside of work I'm a certified marathon coach. So when it became clear Engadget wanted to round up the best wireless workout headphones, I raised my hand. And the timing feels particularly appropriate. Until now I was still using wired buds (old habits die hard), and it happened that every pair I owned was on the fritz.

If you're also tired of taking daily Zoom calls on your laptop, maybe you'd prefer to just turn on the TV, lay back, and learn or conduct business from the couch. Earlier this week, we wrote about a new video device for Microsoft Teams, but it's really large, at 85 inches, and really costly, at $21,199. Amazon is introducing a less pricey option later this year. "I just believe that your big, beautiful TV is a great place for communications and we're going to continue to lean in to make that a better experience well," said Marc Whitten, vice president of Amazon Entertainment Devices and Services. To drive the new device,you'll need the Fire TV Cube, a $119 accessory that's different from the Fire TV streaming stick units.

In what areas is AI technology being used? Yes, artificial intelligence is also being used in agriculture. AI technology performs research and development to achieve yield and increase crop yield. New AI technology also predicts the time of preparation of crops which increases the efficiency of agriculture. Along with this, AI is helpful in monitoring soil and crops.

We care deeply about the success of our customers and strive to help them achieve their goals in inspiring and engaging with their workforce. Sense is seeking a Senior Machine Learning Engineer to deliver our contractor communication platform to the world's best places to work. Sense is a rapidly scaling company making this the best environment to take on ownership as well as learning how to grow a company. The Chatbot team at Sense is building a Recruiting conversational assistant -- a virtual assistant to help job seekers find the right opportunity. Sense's chatbot is responsible for interactions with candidates for help gathering their relevant skills, updating their resumes and identifying those jobs for which they are best suited.

In this post, I will show you how to build a movie recommender program using Python. This will be a simple project where we will be able to see how machine learning can be used in our daily life. If…

We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data. Our representation learning algorithm can be viewed as a simple composition rule that can be applied recursively in a bottom-up fashion to represent arbitrarily complex hierarchical structures in terms of the representations of its constituent components. We demonstrate our ideas on two real world recommendation systems datasets including the one used for the RecSys 2019 challenge. For that dataset, we improve upon the performance achieved by the winning team's model by incorporating embeddings as features generated by our approach in their solution.

Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety of language understanding tasks. However, it is yet unclear if and how these recent models can be harnessed for conducting text-based recommendations. In this work, we introduce RecoBERT, a BERT-based approach for learning catalog-specialized language models for text-based item recommendations. We suggest novel training and inference procedures for scoring similarities between pairs of items, that don't require item similarity labels. Both the training and the inference techniques were designed to utilize the unlabeled structure of textual catalogs, and minimize the discrepancy between them. By incorporating four scores during inference, RecoBERT can infer text-based item-to-item similarities more accurately than other techniques. In addition, we introduce a new language understanding task for wine recommendations using similarities based on professional wine reviews. As an additional contribution, we publish annotated recommendations dataset crafted by human wine experts. Finally, we evaluate RecoBERT and compare it to various state-of-the-art NLP models on wine and fashion recommendations tasks.