Recently, self-attention based models have achieved state-of-the-art performance in sequential recommendation task. Following the custom from language processing, most of these models rely on a simple positional embedding to exploit the sequential nature of the user's history. However, there are some limitations regarding the current approaches. First, sequential recommendation is different from language processing in that timestamp information is available. Previous models have not made good use of it to extract additional contextual information. Second, using a simple embedding scheme can lead to information bottleneck since the same embedding has to represent all possible contextual biases. Third, since previous models use the same positional embedding in each attention head, they can wastefully learn overlapping patterns. To address these limitations, we propose MEANTIME (MixturE of AtteNTIon mechanisms with Multi-temporal Embeddings) which employs multiple types of temporal embeddings designed to capture various patterns from the user's behavior sequence, and an attention structure that fully leverages such diversity. Experiments on real-world data show that our proposed method outperforms current state-of-the-art sequential recommendation methods, and we provide an extensive ablation study to analyze how the model gains from the diverse positional information.

Machine learning is becoming more important in our daily lives. But most of us probably never envisioned a day when it would be important in online dating or the beginning of new relationships. A growing number of video chat services are utilizing machine learning features in interesting ways. MarTech Series published an article last year on the growing relevance of machine learning in video conferencing. The same principles can be just as applicable to video chats with online dating services.

In the first part of this blog, we looked at how Artificial Intelligence (AI) has shifted to the supplier side of the retail eco-system, particularly in two areas -- price and product offering. In this post, we will explore how this has impacted the buyer's journey at almost every stage. As many of you know, the buyer's journey starts from the awareness stage, where he comes to learn about a product or brand, and then goes through the following steps: research, consideration, purchase, and retention; The second is that a company tries to capture its customers. Above all, history shows that people who have previously purchased from your company will be repeat customers if they are happy with the entire journey. AI E-commerce has retained the power to analyze vast ways of data and human behavior.

As Amazon continues its "off-to-college" savings event, Best Buy is offering a couple of Alexa speaker bundles that are great deals if you want the most bang for your buck. The latest among them is a bundle that includes a free Echo Show 5 and a Philips Hue smart light bulb when you buy an Echo Show for $180. Not only do you get those two freebies, but you're getting the Echo Show smart display for $50 off. Best Buy also cut the price of a bundle we covered earlier this month: now you can get an Echo Studio with a free Echo Show 5 and Philips Hue bulb for only $170. Best Buy doesn't advertise the freebies clearly, but you can see the full details of the bundle by clicking the "free items with purchase" link underneath the price on each product page.

Video meet app Zoom on Wednesday announced that it will now be available on smart home displays including Amazon Echo Show, Portal from Facebook and Google Nest Hub Max. Zoom on Portal is expected to be available publicly in September and will arrive on Echo Show and Google Nest Hub Max by the end of the year. "We're excited to bring Zoom to these popular devices. It's more apparent than ever that people are looking for easy-to-use displays for their video communications needs, both professionally and personally," said Oded Gal, Chief Product Officer at Zoom. As a part of Zoom for Home, Zoom users will be able to extend integrated calendar and HD video and audio for Zoom Meetings on these smart displays.

It is expected that innovations such as progressive web apps, artificial intelligence, and augmented/virtual reality will continue to evolve in 2020. Every industry needs some sort of online platform to create a reputation for its business. There are over 1.5 billion websites, including over 200 million popular websites, according to Internet Live Statistics. Yet figures keep on increasing. Through the years the industry has evolved with emerging trends and technologies. To remain ahead of the market, businesses need to concentrate on rising patterns, strategies and solutions to custom site creation.

Multi-room audio specialist Sonos is back with the Arc, the firm's first Dolby Atmos-enabled soundbar that totally transforms your TV's sound. It is a single box of tricks that combines a smart speaker, wifi music sound system and home cinema kit in one, but like most soundbars of this type it can be dogged by audio-picture syncing issues when used with TV set top boxes – more on that later. The Arc looks deceptively simple. It is a sleek, one metre-long cylinder that is surprisingly compact considering there are eight separate woofers, three tweeters and a collection of electronics all hidden behind the matt metal mesh. Four of the woofers face you directly through the front of the Arc.

In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE. The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation. Specifically, we first construct a unified knowledge graph and extract key entities between user--product pairs, which serve as the skeleton of a conversation. Then we simulate conversations mirroring the human coarse-to-fine process of choosing preferred items. The proposed baselines and experiments demonstrate that our dataset is able to provide innovative opportunities for conversational recommendation.

Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of "value" that is worth optimizing for. We use the framework of measurement theory to (a) confront the designer with a normative question about what the designer values, (b) provide a general latent variable model approach that can be used to operationalize the target construct and directly optimize for it, and (c) guide the designer in evaluating and revising their operationalization. We implement our approach on the Twitter platform on millions of users. In line with established approaches to assessing the validity of measurements, we perform a qualitative evaluation of how well our model captures a desired notion of "value".

In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution. However, many of these text-oriented methods rely heavily on the availability of text information for every user and item, which obviously does not hold in real-world scenarios. Furthermore, specially designed network structures for text processing are highly inefficient for on-line serving and are hard to integrate into current systems. In this paper, we propose a flexible neural recommendation framework, named Review Regularized Recommendation, short as R3. It consists of a neural collaborative filtering part that focuses on prediction output, and a text processing part that serves as a regularizer. This modular design incorporates text information as richer data sources in the training phase while being highly friendly for on-line serving as it needs no on-the-fly text processing in serving time. Our preliminary results show that by using a simple text processing approach, it could achieve better prediction performance than state-of-the-art text-aware methods.