To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters.

Recommender systems today have become an essential component of any commercial website. Collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely used in recommender systems. However, the natural data sparsity problem limits their performance where users generally interact with very few items in the system. Consequently, multiple hybrid models were proposed recently to optimize MF performance by incorporating additional contextual information in its learning process. Although these models improve the recommendation quality, there are two primary aspects for further improvements: (1) multiple models focus only on some portion of the available contextual information and neglect other portions; (2) learning the feature space of the side contextual information needs to be further enhanced. In this paper, we propose a Collaborative Dual Attentive Autoencoder (CATA++) for recommending scientific articles. CATA++ utilizes an article's content and learns its latent space via two parallel autoencoders. We use attention mechanism to capture the most pertinent part of information in making more relevant recommendations. Comprehensive experiments on three real-world datasets have shown that our dual-way learning strategy has significantly improved the MF performance in comparison with other state-of-the-art MF-based models according to various experimental evaluations. The source code of our methods is available at: https://github.com/jianlin-cheng/CATA.

Gone are the days when you get up and turn the dial or press some buttons on your thermostat to get the airflow going in your home. Thanks to popular voice assistants like Amazon's Alexa and Google Assistant--and the invention of smart thermostats--controlling your Sensi smart thermostat is easier than ever when you follow these simple steps. Skip further down the article to see instructions for setting up with Google Assistant. Alexa is the most popular smart assistant out there, and she can help you control your Sensi smart thermostat. Create a Sensi account using a valid email address and a strong password.

In many applications of recommendation systems, we have data in the form of an incomplete matrix, where one dimension is growing and the other dimension is fixed. For instance, in recommendation systems, there is a fixed set of potential products (rows of a matrix) to offer customers that arrive over time (columns of a matrix). Three other applications are choosing machine learning models (rows) for each new customer's dataset (columns) [FSE18], choosing which survey questions (rows) to ask to respondents (columns) that arrive sequentially [ZTCS19], or choosing which lab tests (rows) to order for each new patient (columns) [HL14]. In these cases, there is an inherent asymmetry with respect to the dimensions in the budget: we have a budget over each column, not over each row. We could choose any machine learning model and recommend it for each dataset, or choose any survey question and give it to every user, but it is very hard to run every machine learning pipeline on an arbitrary dataset, or to give every survey question to an arbitrary respondent (indeed, in [ZTCS19], users omitting too many answers was the precise motivation for their problem).

Artificial intelligence in financial payments will be explored fully in March by the AI In Financial Services event and the AI In Finance Summit event in March. The AI In Financial Services event is scheduled for March 17th at the America Square Conference Centre in London. The AI in Finance Summit is slated for March 31 to April 1 at the etc Venues, also in London. The Technova AI In Financial Services event will focus on business transformation to robotic automation, customer innovation to ethical transparency, equipping attendees with the skills and expertise to capitalize on the artificial intelligence revolution, notes RAM Research and PYRPTS. Among noted speakers is Kathy Liu, Head In Innovation, HSBC Global Operations.

One in every four adults in America now owns a voice-activated smart speaker. While we love the convenience of talking to a gadget to play music, make calls, and such, most of us get a little creeped out when they "wake up" when they're not supposed to. I often trigger Siri when saying "seriously," or "Suli" – the name of my parents' dog. My friend Bill Keeshan says his Alexa connected device "gets triggered by my daughter saying'actually.'" All too familiar anecdotes aside, researchers at Northeastern University and the Imperial College of London spent the last six months streaming 125 hours of popular Netflix TV shows to a handful of voice-activated smart speakers.

You might be wondering what exactly Braze.com is and why it grabs their geographic, associations and more. I found out Friday that after I composed a message on the PayPal-owned digital payments app Venmo to pay my personal trainer Jarek, Venmo passed on my geographic locations and associations (including Jarek) to Braze, which calls itself a "customer engagement" company. Just last month, the Norwegian Consumer Council issued a blistering report showing what happened to users of the dating sites OKCupid and Grindr in the background, after people revealed all about their interests. OkCupid "shared highly personal data about sexuality, drug use, political views, and more," with Braze, according to the report. Grindr, a popular dating and social app used by gay and bisexual men, sent data to Braze about the "relationship type" men were seeking on the app, per the report.

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

AI appears neutral, but it's made by humans, which means it internalizes all the same bias as we have - including gender bias. AI is a mirror of ourselves. According to Harvard Business Review, there have been many incidences of AI adopting gender bias from humans. Harvard Business Review cites an example of natural language processing (NLP) that is present in Amazon's Alexa and Apple's Siri. Harvard Business Review also cites word-embeddings as a bias aspect of AI.