Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical classes or tokens. The standard approach is to learn end-to-end, dense latent representations or embeddings for each token. The resulting embeddings require large amounts of memory that blow up with the number of tokens. Training and inference with these models create storage, and memory bandwidth bottlenecks leading to significant computing and energy consumption when deployed in practice. To this end, we present the problem of \textit{Memory Allocation} under budget for embeddings and propose a novel formulation of memory shared embedding, where memory is shared in proportion to the overlap in semantic information. Our formulation admits a practical and efficient randomized solution with Locality sensitive hashing based Memory Allocation (LMA). We demonstrate a significant reduction in the memory footprint while maintaining performance. In particular, our LMA embeddings achieve the same performance compared to standard embeddings with a 16$\times$ reduction in memory footprint. Moreover, LMA achieves an average improvement of over 0.003 AUC across different memory regimes than standard DLRM models on Criteo and Avazu datasets

Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific \emph{goals} such as increasing the transparency of a recommendation or increasing a user's trust in the recommender system. In this paper, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of preference aggregation strategies.

Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipment. In this paper we show how divide-and-conquer based (direct) diagnosis algorithms (no conflict detection is needed) can be exploited in constraint-based recommendation scenarios. In this context, we provide an overview of the MediaWiki-based recommendation environment WeeVis.

Nowadays, we see recommendation systems everywhere. When you buy something in an online marketplace like Amazon, eBay, or any other place, they suggest similar products. On Netflix or youtube, you see the suggestions on your homepage similar to your previous activities or searches. They all follow this one idea. That is they take data from your previous activities and run a similarity analysis.

Earlier we saw many data science applications. Today we will see the diverse data science use cases. We will take examples of social media, e-commerce, transportation, and healthcare to demonstrate some of the important data science use cases in contemporary industries. Stay updated with latest technology trends Join DataFlair on Telegram!! Data Science has brought another industrial revolution to the world. Every industry in this world requires data.

How do you teach an AI to walk? Artificial Intelligence, as we typically use the term right now, means a computational system that learns through pattern-spotting and self-correction, so you don't so much teach it as create a setting in which it can teach itself. If you want an AI to walk, you provide a set of constraints -- gravity exists, bodies are made of connected parts, the ground pushes back when you push on it -- and give it a challenge, like moving a certain distance. Then you step back and let it learn, and often marvel at the results. A recent paper entitled "The Surprising Creativity of Digital Evolution," published by a conglomerate of European and North American researchers, is packed with technically correct AI-devised solutions to the locomotion problem that are also, by any traditional measure, wrong.

Every evening last summer, after I'd shut down my work laptop, my 3-year-old daughter and I would approach our Google Home smart speaker and yell, "Hey Google, can you play'Aankh Marey' from the movie Simmba?" We'd hold our breaths and wait for a response. The digital assistant would then repeat the name of the Bolly wood song we'd requested in its default standard American accent. We'd rejoice and dance when the assistant played the right number, which happened about half the time. My daughter was going to a Bollywood dance class and we'd finally found a use for the device that my husband had won at a tech conference. Often, however, it would mishear our requests and play something else.

The adoption of technology has changed the way we connect and converse with others in our society and dating is no exception. The prevalence of smart phones mean we are always contactable, social media allows others to get to know us before we have even met, and dating apps give us an abundance of choice in a suitable partner or partners. This article focuses on how technology has changed dating. How did your parents meet? Mine met on a double blind date in which my mother and father had mutual friends who introduced them.

This is only a partial data set. Eighteen forces, including some of the country's biggest like the Metropolitan Police, Greater Manchester Police, PSNI, and Police Scotland, did not provide data, meaning the actual crime numbers are likely to be significantly higher.

I want smart glasses to be a thing. So far, every real pair of smart glasses has fallen woefully short of the mark. Amazon's Echo Frames are the latest smart glasses to follow in that storied tradition of overpromising and underdelivering. They are essentially an Echo Dot that you wear on your face--built entirely around interacting with Amazon's voice assistant, Alexa. They don't have a screen in the lenses like the Focals by North or most other smart eyewear.