Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this paper, we devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe). To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice and exploits time-sliced graph neural networks to learn user and item representations. Moreover, to enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices based on temporal point process. Comprehensive experiments on three public real-world datasets demonstrate DRL-SRe outperforms the state-of-the-art sequential recommendation models with a large margin.

Google is updating critical features for the millions of drivers who depend on its technology to help them get around. The tech giant announced the upcoming changes Thursday to Google Assistant and Android Auto driving modes and a new automaker, Honda, will have Google technology installed in its vehicles. Google said that drivers using Google Assistant on Android phones will soon see a new dashboard they say will reduce "the need to fiddle with your phone while also making sure you stay focused on the road." Instead of scrolling while driving, Google said drivers could tap to see who just called or sent a text and have access to several apps to listen to music with the new dashboard. The dashboard will also include a new messaging update where drivers can say, "Hey Google, turn on auto-read," to hear their new messages read aloud when they come in and respond by voice.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

This is the first post in an intended series on what is the current state of Artificial Intelligence capabilities, and what we can expect in the relative short term. I will be at odds with the more outlandish claims that are circulating in the press, and amongst what I consider an alarmist group that includes people in the AI field and outside of it. In this post I start to introduce some of the key components of my future arguments, as well as show how different any AI system might be from us humans. Some may recognize the title of this post as an homage to the 1974 paper by Thomas Nagel, "What Is It Like to Be a Bat?". Two more recent books, one from 2009 by Alexandra Horowitz on dogs, and one from 2016 by Peter Godfrey-Smith on octopuses also pay homage to Nagel's paper each with a section of a chapter titled "What it is like", and "What It's Like", respectively, giving affirmative responses to their own questions about what is it like to be a dog, or an octopus.

After playing through some Billie Eilish tracks in Beat Saber, soon you'll also be able to kick back and listen to a limited-edition Echo Studio sporting the cover of her latest album, "Happier Than Ever." Beyond the beige fabric and Eilish's visage, the $230 speaker is no different than the standard $200 Echo Studio. That's a shame if you were hoping for some sort of upgrade, but if it's any consolation, we adored the Echo Studio's beefy hardware when it launched two years ago. It's one of the few smart speakers built for 3D Audio, and it has more than enough power to blast all of your favorite tunes. The Billie Eilish Limited-Edition Echo Studio also comes with a six-month subscription to Amazon Music, typically a $48 value.

Amazon Advertising helps companies build their brand and connect with shoppers, through ads shown both within and beyond Amazon's store, including websites, apps, and streaming TV content in more than 15 countries. Businesses or brands of all sizes including registered sellers, vendors, book vendors, Kindle Direct Publishing (KDP) authors, app developers, and agencies on Amazon marketplaces can upload their own ad creatives, which can include images, video, audio, and of course products sold on Amazon. To promote an accurate, safe, and pleasant shopping experience, these ads must comply with content guidelines. Can you figure out why two of the following ads would not be compliant? It also shows the same product multiple times.

GM will soon allow OnStar subscribers to contact emergency services through Alexa smart speakers. The company is bringing the OnStar Guardian Alexa skill to Amazon devices in the coming months. If you need emergency assistance, you're an OnStar member and the skill is active, you can say "Alexa, call for help." OnStar emergency-certified advisors can then call police or EMTs for you. Alexa devices don't support 911 calls otherwise, but you can set up an emergency contact.

If you are a student or a professional looking for various open-source Natural Language Processing (NLP) projects, then, this article is made to help you. The NLP projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Text Summarizer is a project that can summarize long paragraphs of text into a single line summary. It can turn an article into a summary using Python and Keras library.

How to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-based methods, a tree structure T is adopted as index and each item in corpus is attached to a leaf node on T . Then the recommendation problem is converted into a hierarchical retrieval problem solved by a beam search process efficiently. In this paper, we argue that the tree index used to support efficient retrieval in tree-based methods also has rich hierarchical information about the corpus. Furthermore, we propose a novel context-aware tree-based deep model (ConTDM) for recommender systems. In ConTDM, a context-aware user preference prediction model M is designed to utilize both horizontal and vertical contexts on T . Horizontally, a graph convolutional layer is used to enrich the representation of both users and nodes on T with their neighbors. Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval. Besides, we argue that the proposed user preference model in ConTDM can be conveniently extended to other tree-based methods for recommender systems. Both experiments on large scale real-world datasets and online A/B test in large scale industrial applications show the significant improvements brought by ConTDM.

Low-rank matrix approximation is one of the central concepts in machine learning, with applications in dimension reduction, de-noising, multivariate statistical methodology, and many more. A recent extension to LRMA is called low-rank matrix completion (LRMC). It solves the LRMA problem when some observations are missing and is especially useful for recommender systems. In this paper, we consider an element-wise weighted generalization of LRMA. The resulting weighted low-rank matrix approximation technique therefore covers LRMC as a special case with binary weights. WLRMA has many applications. For example, it is an essential component of GLM optimization algorithms, where an exponential family is used to model the entries of a matrix, and the matrix of natural parameters admits a low-rank structure. We propose an algorithm for solving the weighted problem, as well as two acceleration techniques. Further, we develop a non-SVD modification of the proposed algorithm that is able to handle extremely high-dimensional data. We compare the performance of all the methods on a small simulation example as well as a real-data application.