A recommendation system is a type of information filtering system. By drawing from huge data sets, the system's algorithm can pinpoint accurate user preferences. Once you know what your users like, you can recommend them new, relevant content. Netflix, YouTube, Google, Amazon etc are all examples of recommendation systems in use. The systems provide users with relevant suggestions based on the choices they make.

One of the biggest challenges facing people new to building recommender systems is the lack of understanding around what these systems look like in the real world. The majority of the online content around recommender systems focuses on models and is often limited to a simple example of collaborative filtering. For new practitioners, there is an enormous gap between examples of simple models and a production system that serves recommendations. In this blog post we'll share a pattern that we feel covers the majority of recommender systems deployed today with examples from companies like Meta, Netflix, and Pinterest. This pattern is central to how we think about building end-to-end recsys within the NVIDIA Merlin team and we're excited to share it with the broader community and help build an understanding and consensus of what recommender systems (not just models) look like in production.

A recommendation system is one of the applications of data science that is used by almost any application or website today. Many websites today use a recommendation system to recommend articles to their readers. For example, Medium.com and even the website you're currently reading this article on is also using a recommendation system to recommend articles to its readers. So, if you want to learn how to create an article recommendation system with machine learning, this article is for you. In this article, I will walk you through how to build an Article Recommendation System with Machine Learning using Python.

AI, or artificial intelligence, is a term for the simulation of human intelligence in machines. In marketing, AI is used to automate and personalize interactions with customers. This can be done through chatbots, which use natural language processing to communicate with customers, or through recommendation engines, which use machine learning to create personalized product recommendations. AI can also be used to target ads and create customer profiles. As humans, we are constantly bombarded with marketing messages.

What comes to mind when you think of Artificial Intelligence (AI)? Chances are, the first thing that pops in your head is a group of robots that take over the planet and rule over humans. Thanks to the reoccurring themes in Hollywood and the media, AI has gotten a bad rap as something that is created by evil corporations but wreaks havoc on all of humanity in the future. However, you might be forgetting that AI is already embedded in your day-to-day routine. Autocorrect, self-driving cars, robo advisors, and of course--Siri and Alexa--are all under the umbrella of AI.

Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Amber Heard's former personal assistant accused the "Aquaman" actress of creating an abusive work environment in a videotaped deposition played Thursday during actor Johnny Depp's defamation trial against his ex-wife in Fairfax, Virginia. Depp, 58, is suing Heard, 35, for $50 million over an op-ed she wrote for the Washington Post alleging she was the victim of domestic abuse. Heard never identified Depp directly, but attorneys for the "Pirates of the Caribbean" actor say her allegations have negatively impacted Depp's career and relationship with his family.

It seems pretty much anything can be made smart these days – and that goes for services as well as products. This "cognification" of products and services is a major consumer trend that's impacting all industries. In other words, whatever your sector, you can be sure your customers will soon expect more intelligent offerings (that's if they don't already expect it). Let's look at the smarter products, smarter services trend, and see how other businesses are embracing it. You can slap the "smart" label on anything, but for a product to be truly intelligent, in my view, it needs to be connected.

We in pharma are all about improving our customer experience and customer engagement. A lot is being done in this area. However, if you really want to put your customer experience and customer engagement on steroids, you need to consider using AI in the process. For the past decade, pharma companies have employed one-to-many communications using social media in their customer engagement efforts. There is a great opportunity for companies to use conversational AI for ongoing customer engagement rather than just a one off transactional point in time. I mean conversations that use natural language with a conversational flow, not'Push 1 to discuss X, Push 2 to discuss Y' prompts.

Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of information about multiple factors, including user's interests, item attributes along with confounding factors such as user conformity, and item popularity. Considering these entangled representations for inferring user preference may lead to biased recommendations (e.g., when the recommender model recommends popular items even if they do not align with the user's interests). Recent research proposes to debias by modeling a recommender system from a causal perspective. The exposure and the ratings are analogous to the treatment and the outcome in the causal inference framework, respectively. The critical challenge in this setting is accounting for the hidden confounders. These confounders are unobserved, making it hard to measure them. On the other hand, since these confounders affect both the exposure and the ratings, it is essential to account for them in generating debiased recommendations. To better approximate hidden confounders, we propose to leverage network information (i.e., user-social and user-item networks), which are shown to influence how users discover and interact with an item. Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders. Experiments on real-world datasets validate the effectiveness of the proposed model for debiasing recommender systems.

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