Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications.

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.

Embedding based product recommendations have gained popularity in recent years due to its ability to easily integrate to large-scale systems and allowing nearest neighbor searches in real-time. The bulk of studies in this area has predominantly been focused on similar item recommendations. Research on complementary item recommendations, on the other hand, still remains considerably under-explored. We define similar items as items that are interchangeable in terms of their utility and complementary items as items that serve different purposes, yet are compatible when used with one another. In this paper, we apply a novel approach to finding complementary items by leveraging dual embedding representations for products. We demonstrate that the notion of relatedness discovered in NLP for skip-gram negative sampling (SGNS) models translates effectively to the concept of complementarity when training item representations using co-purchase data. Since sparsity of purchase data is a major challenge in real-world scenarios, we further augment the model using synthetic samples to extend coverage. This allows the model to provide complementary recommendations for items that do not share co-purchase data by leveraging other abundantly available data modalities such as images, text, clicks etc. We establish the effectiveness of our approach in improving both coverage and quality of recommendations on real world data for a major online retail company. We further show the importance of task specific hyperparameter tuning in training SGNS. Our model is effective yet simple to implement, making it a great candidate for generating complementary item recommendations at any e-commerce website.

The holiday shopping season reaches its peak when Cyber Monday deals roll around. Sales are often better than on Black Friday, but with every retailer promising the best Cyber Monday discounts, it can be tricky to work out what deals to snag and when to pass. Luckily, we have done the hard work for you. WIRED reviewers try countless gadgets, tools, and digital delights of all kinds every week, and we have developed smart shopping tips and tricks to weed out fake discounts and bring you the real deals. We can say with confidence that these are the absolute best Cyber Monday deals you're going to find. You will find regular updates as products go out of stock and prices change, and we'll keep scouring to find more deals worth grabbing. Updated November 27: We've added deals on umbrellas, mobile accessories, and more. We've also updated pricing and availability. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide through Cyber Monday. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Just like upgrading the bed you sleep on, few things will improve your life like a good chair. If you park your body in front of a desk all day, it's a good idea to give the humble chair more attention. Some of our favorites are on sale right now. See our Best Office Chairs guide for more. Our top office chair pick is a bit cheaper right now. We've tested more than 35 office chairs in the past year and this is the one to get based on comfort, quality, adjustability, and price. At this price, you'd be hard-pressed to find a cheaper way to get seven points of adjustment. The only thing we don't like is that pet hair tends to cling, so keep a lint roller handy. The Zeph looks wonderful--there are dozens of color customizations--but the only adjustment you can make is to raise the seat up or down. You might think that would make for an uncomfortable chair, but it doesn't. This lack of adjustability is by design as the Zeph is designed to mold to your body. We do suggest you get the seat pad and arms, which add a smidge more comfort, though it does jack the price up a bit. The Zeph is compact, making it a great option for smaller spaces. The humble Hyken is our favorite budget office chair. It reclines, has a breathable mesh fabric on the back and seat, and it's sturdy.

Long gone are the days of camping outside Walmart after Thanksgiving, making small talk with strangers as you prepare to jostle them in the hopes of snagging a doorbuster deal. Swap the coat for pajamas and the tent for your couch--all of the best Walmart Cyber Monday deals are available online. You can even choose free in-store pickup to skip waiting for the mail to come. Updated November 27: We've added the Instax Mini 7, Vitamix 6500 blender, and an Anker Portable Charger. We've also updated pricing and availability throughout. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide through Cyber Monday.

The holiday shopping season reaches its peak when Cyber Monday deals roll around. Sales are often better than on Black Friday, but with every retailer promising the best Cyber Monday discounts, it can be tricky to work out what deals to snag and when to pass. Luckily, we have done the hard work for you. WIRED reviewers try countless gadgets, tools, and digital delights of all kinds every week, and we have developed smart shopping tips and tricks to weed out fake discounts and bring you the real deals. We can say with confidence that these are the absolute best Black Friday and Cyber Monday deals you're going to find this weekend. You will find regular updates as products go out of stock and prices change, and we'll keep scouring to find more deals worth grabbing. Updated November 27, 2022: We've added deals on umbrellas, mobile accessories, and more. We've also updated pricing and availability. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Just like upgrading the bed you sleep on, few things will improve your life like a good chair. If you park your body in front of a desk all day, it's a good idea to give the humble chair more attention. Some of our favorites are on sale right now. See our Best Office Chairs guide for more. Our top office chair pick is a bit cheaper right now. We've tested more than 35 office chairs in the past year and this is the one to get based on comfort, quality, adjustability, and price. At this price, you'd be hard-pressed to find a cheaper way to get seven points of adjustment. The only thing we don't like is that pet hair tends to cling, so keep a lint roller handy. The Zeph looks wonderful--there are dozens of color customizations--but the only adjustment you can make is to raise the seat up or down. You might think that would make for an uncomfortable chair, but it doesn't. This lack of adjustability is by design as the Zeph is designed to mold to your body. We do suggest you get the seat pad and arms, which add a smidge more comfort, though it does jack the price up a bit. The Zeph is compact, making it a great option for smaller spaces.

This time of year, all the cute Christmas decor and ornaments are out, which makes trips even more fun. Shopping on its website isn't as fun, sadly, but it's still packed full of holiday sales. Target's Black Friday and Cyber Monday deals have been live for almost every day of the month, but several discounts are even sweeter now. Updated on November 26, 2022: We've added some navigation, the Bose QC Earbuds, and checked over the entire post. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Black Friday and Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. The retailer is offering a price match guarantee this season. If you purchase an item in-store or online from now through December 24 and the price goes lower, you can request a price match.

As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model. However, we find such a standard distillation paradigm would incur serious bias issue -- popular items are more heavily recommended after the distillation. This effect prevents the student model from making accurate and fair recommendations, decreasing the effectiveness of RS. In this work, we identify the origin of the bias in KD -- it roots in the biased soft labels from the teacher, and is further propagated and intensified during the distillation. To rectify this, we propose a new KD method with a stratified distillation strategy. It first partitions items into multiple groups according to their popularity, and then extracts the ranking knowledge within each group to supervise the learning of the student. Our method is simple and teacher-agnostic -- it works on distillation stage without affecting the training of the teacher model. We conduct extensive theoretical and empirical studies to validate the effectiveness of our proposal. We release our code at: https://github.com/chengang95/UnKD.

Intrigued by an Ars Technica post about Amazon's Alexa that suggested all was not well in the tech company's division that looks after its smart home devices, I went rooting in a drawer where the Echo Dot I bought years ago had been gathering dust. Having found it, and set it up to join the upgraded wifi network that hadn't existed when I first got it, I asked it a question: "Alexa, why are you such a loss-maker?" To which she calmly replied: "This might answer your question: mustard gas, also known as Lost, is manufactured by the United States." At which point, I solemnly thanked her, pulled the power cable and returned her to the drawer, where she will continue to gather dust until I can think of an ecologically responsible way of recycling her. I bought the device on 5 December 2016 (on the basis that one shouldn't pontificate on kit that one hasn't purchased oneself) and wrote about it in January 2017.

Intrigued by an Ars Technica post about Amazon's Alexa that suggested all was not well in the tech company's division that looks after its smart home devices, I went rooting in a drawer where the Echo Dot I bought years ago had been gathering dust. Having found it, and set it up to join the upgraded wifi network that hadn't existed when I first got it, I asked it a question: "Alexa, why are you such a loss-maker?" To which she calmly replied: "This might answer your question: mustard gas, also known as Lost, is manufactured by the United States." At which point, I solemnly thanked her, pulled the power cable and returned her to the drawer, where she will continue to gather dust until I can think of an ecologically responsible way of recycling her. I bought the device on 5 December 2016 (on the basis that one shouldn't pontificate on kit that one hasn't purchased oneself) and wrote about it in January 2017.