In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.

Artificial intelligence (AI) is responsible for driving autonomous vehicles, powering intelligent assistants such as Alexa and Siri, and placing annoying advertisements on web pages. AI has also improved many aspects of pediatric medicine, and played an important role in the COVID-19 pandemic. Voice recognition/dictation software is an example of AI that is currently used in pediatric practice. Today, Dragon Medical One from Nuance Communications, the most widely used voice recognition medical software, boasts a vocabulary of 300,000 words and integrates vocabularies for 90 medical specialties. By integrating deep learning (DL), the software covers the nuances of the user's speech patterns and improves over time, achieving 99% accuracy.1

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The Command Prompt app has been around since December 1987, providing Windows users with a command-line interface from which to execute operating systems tasks, many of which are very useful. Due to its popularity, Microsoft has made the app easily accessible on Windows 10, in more ways than one. Here are 11 ways to open Command Prompt. You can search for any app using the Windows search bar, and the Command Prompt is no exception. You can also open Command Prompt from the Start menu.

The app has added features to find matches in other ways, profiles that allow users to record snippets on their interests to Tinder Passport, a paid product that gives users the chance to find matches across the world. The company has also expanded into interactive dating features, such as Vibes, a 48-hour event in the app that asks users to respond to a series of questions to match with others who participate. The new Explore section includes more than 15 types of interests, such as "foodies," "gamers," and "animal parents." Different interests will surface to users depending on their locations, the time of day and their own passions. It is available to Tinder users in the U.S., U.K, Australia and New Zealand to start.

Every time you search for something in Google, artificial intelligence is working behind the scenes to generate responses to your query. A deep learning system called RankBrain has changed the way the search engine functions. In many cases, RankBrain handles search queries better than traditional algorithmic rules that were hand-coded by human engineers, and Google realized a long time ago that AI is the future of their search platform. AI will try to understand exactly what we are searching for and then deliver personalized results to us, based on what it knows about us. You may not realize it, but AI is already deeply integrated into many of the Google products you are using today.

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.

This is the final part of our 4-part series! In the previous 3, we covered the theory of collaborative filtering, how to build a Flask API, and how to deploy the API on AWS ECS. In this post, we'll build a simple Vue.js frontend that aims to simplify the movie recommendations as much as possible. Hence, we'll only ask the user to enter their favourite movie and recommend movies that are similar to it. Key functionalities of this project are the auto-search function for finding movie titles from MovieLens dataset and leveraging open-source scrapped movie posters to display recommended movies by the backend API.

To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted considerable attention recently. However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability. This is because the construction of these KGs is independent of the collection of historical user-item interactions; hence, information in these KGs may not always be helpful for recommendation to all users. In this paper, we propose attentive Knowledge-aware Graph convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR). CG-KGR is a novel knowledge-aware recommendation model that enables ample and coherent learning of KGs and user-item interactions, via our proposed Collaborative Guidance Mechanism. Specifically, CG-KGR first encapsulates historical interactions to interactive information summarization. Then CG-KGR utilizes it as guidance to extract information out of KGs, which eventually provides more precise personalized recommendation. We conduct extensive experiments on four real-world datasets over two recommendation tasks, i.e., Top-K recommendation and Click-Through rate (CTR) prediction. The experimental results show that the CG-KGR model significantly outperforms recent state-of-the-art models by 4.0-53.2% and 0.4-3.2%, in terms of Recall metric on Top-K recommendation and AUC on CTR prediction, respectively.

Bob Smith sits upright on the sofa as his grandson, Louis Brow, prepares to quiz him on youth slang. We are sitting in the living room of Louis's family home in Ilkley, West Yorkshire; Bob has travelled over from Walton-le-Dale, Lancashire, in his Nissan Micra. "Would you know what flexing is?" Louis begins. "If I was to flex on the Gram?" "You're bending, you're a contortionist," suggests Bob, gamely. "Nowadays it's someone showing off," Louis explains. I might say to my mate, that's a big flex, you're flexing, you're looking good." "Why not say the correct word?" Bob might not know the terminology, but he has had a major flex on social media recently. Louis is the Yorkshire Challenge belt (69kg) boxing champion and credits his success to the outdoor boxing gym, nicknamed the Dojo, that Grandad Bob helped him build in the back yard of the family home. A TikTok video of Louis using a tyre as a punchbag while his grandfather eggs him on went viral this year. Today, Louis confidently reels off his social media wins to Bob. He just hit 1m likes on TikTok; their video garnered 2.4m views. "Well, people like watching things," Bob says, sagely. Talk turns to another modern phenomenon: dating apps. Bob explains what dating was like in his youth. "There would be these dos in the church with disco dancing.