Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. On the other hand, in recent years, increasing research efforts in machine learning and data mining have been dedicated to dealing with data involving interactions from two or more factors. However, few researchers have addressed the heterogeneous relationship in data brought by modulation of multiple factors using techniques of DDP. In this paper, we propose a novel technique, MultiLinear Dirichlet Processes (MLDP), to constructing DDPs by combining DP with a state-of-the-art factor analysis technique, multilinear factor analyzers (MLFA). We have evaluated MLDP on real-word data sets for different applications and have achieved state-of-the-art performance. Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. To introduce dependency into DDP, various techniques have been developed via correlating through components of atomic measures, such as atom sizes [8], [11], [23] and atom locations [6], [10], [28], sampling from a DP with random distributions as atoms [24], operating on underlying compound Poisson processes [18], regulating by Lévy Copulas [16], or constructing those measures through a mixture of several independent measures drawn from DPs [12], [15], [19], [20].

At $100, Google's impressive Nest Audio was already good value for money, but a 25 percent discount has now brought it down to an even more affordable $75. Perhaps, it's a case of Google countering Amazon's early Prime Day deals on Echo speakers with a new all-time low price. Whatever the reasons behind the latest promo, it's always nice to have another option when shopping for tech. The Nest Audio is a solid bet for music fans on a budget. As we noticed in our tests, it's slightly louder than Apple's HomePod Mini and packs stronger bass, too. Inside the speaker is a 75mm woofer and 19mm tweeter, while the Google Home and Nest Mini make do with single drivers.

We introduce StockBabble, a conversational agent designed to support understanding and engagement with the stock market. StockBabble's value and novelty is in its ability to empower retail investors -- many of which may be new to investing -- and supplement their informational needs using a user-friendly agent. Users have the ability to query information on companies to retrieve a general and financial overview of a stock, including accessing the latest news and trading recommendations. They can also request charts which contain live prices and technical investment indicators, and add shares to a personal portfolio to allow performance monitoring over time. To evaluate our agent's potential, we conducted a user study with 15 participants. In total, 73% (11/15) of respondents said that they felt more confident in investing after using StockBabble, and all 15 would consider recommending it to others. These results are encouraging and suggest a wider appeal for such agents. Moreover, we believe this research can help to inform the design and development of future intelligent, financial personal assistants.

You won't find it written anywhere in the press materials, but the Beats Studio Buds may as well be called the AirPods 3. From their funky new design and minimalist theme to Apple-ready features like voice-activated Siri, these are immediately the obvious choice right now for AirPods fanatics who don't want to spend up for the AirPods Pro. Wisely, though, Apple has crafted these buds to be nearly as useful for either side of the mobile aisle, including native one-touch pairing for Android or iPhone, a simple but useful Android app, and quick access to alternative voice assistants. The result is a pair of buds that easily outdo the aging AirPods, no matter which phone you choose. The beats have a new pill-like design at their exterior for controls as well as easy insertion. The last Beats earbuds, the Powerbeats, were simply a step-down version of the Powerbeats Pro, chaining the clip-ons together for a more affordable workout option.

Doubling up on Amazon's Echo speakers has its perks. You can pair them together to get stereo sound or use them to create a Home Theatre setup for your Fire TV. To get you to multiply, Amazon is offering two Echo Dots for $50 -- which is how much you'd normally pay for one -- as part of an early Prime Day sale. If you're signed up to Prime, you can grab the discount by using the code PDDOT2PK at checkout. Those seeking even bigger sound, should check out the $120 deal on two Echo speakers, activated with the ECHOPRIME code and also exclusive to Prime members.

The latest version of Amazon Echo Buds only arrived a month ago, but the company is already offering them at a discount just ahead of Prime Day. It's the first time that the second-gen Echo Buds have been on sale. A bundle with the wireless charging case is also $40 off at $100. In our second-gen Echo Buds review, we found that the true wireless earbuds were an upgrade from the original model on almost all fronts. Along with a smaller design, they offer true active noise cancellation (ANC) and better audio quality than the first-gen Echo Buds.

Amazon's Echo Show 5 just got an upgrade, but if you don't mind missing out on relatively minor additions like a higher-resolution camera, you can pick up the first-gen model at its lowest price to date. Just ahead of Prime Day, the 2019 Echo Show 5 is available for $45. Amazon has been selling the device for $50 recently, but we saw it drop to $45 during Prime Day and Black Friday last year. As the name suggests, Echo Show 5 has a smaller screen than other devices in the lineup at 5.5 inches. It's primarily designed for desks and nightstands.

Implementation of Machine learning algorithms to assist consumers in discovering new products and services is new form of digital marketing on e-commerce website. When you shop online, a recommendation system directs you to the product that is most likely to be purchased.Users are often frustrated by options and need assistance discovering what they're looking for, so recommender systems are an important element of our digital environment. Customers will be happier, and profits will increase as a result. Recommender systems are similar to salespeople who know what you want based on your history and interests. In this blog, I am going to show different approaches for Recommendation system that can built on Brazilian e-commerce public dataset.

Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences towards items. However, designing a proper CF model for a given data is not easy, since the properties of datasets are highly diverse. In this paper, motivated by the recent advances in automated machine learning (AutoML), we propose to design a data-specific CF model by AutoML techniques. The key here is a new framework that unifies state-of-the-art (SOTA) CF methods and splits them into disjoint stages of input encoding, embedding function, interaction function, and prediction function. We further develop an easy-to-use, robust, and efficient search strategy, which utilizes random search and a performance predictor for efficient searching within the above framework. In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones. Extensive experiments on five real-world datasets demonstrate that our method can consistently outperform SOTA ones for various CF tasks. Further experiments verify the rationality of the proposed framework and the efficiency of the search strategy. The searched CF models can also provide insights for exploring more effective methods in the future

Abstract: Conversational agents are a recent trend in human-computer interaction, deployed in multidisciplinary applications to assist the users. In this paper, we introduce "Atreya", an interactive bot for chemistry enthusiasts, researchers, and students to study the ChEMBL database. Atreya is hosted by Telegram, a popular cloud-based instant messaging application. This user-friendly bot queries the ChEMBL database, retrieves the drug details for a particular disease, targets associated with that drug, etc. This paper explores the potential of using a conversational agent to assist chemistry students and chemical scientist in complex information seeking process.