Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings. However, user/item diversities in social recommendations are not well utilized in the literature. Especially, inter-factor (social and rating factors) relations and distinct rating values need taking into more consideration. In this paper, we propose an attentive social recommendation system (ASR) to address this issue from two aspects. First, in ASR, Rec-conv graph network layers are proposed to extract the social factor, user-rating and item-rated factors and then automatically assign contribution weights to aggregate these factors into the user/item embedding vectors. Second, a disentangling strategy is applied for diverse rating values. Extensive experiments on benchmarks demonstrate the effectiveness and advantages of our ASR.

Although recommender systems have received great success, We categorize recommender systems as shallow it is well known for highly skewed datasets, models and deep models. The first class engineers and researchers need to adjust their incorporates shallow machine learning technologies methods to tackle the specific problem to yield good such as matrix factorization and learning to rank, results. Inability to deal with highly skewed dataset while the second class are deep learning models like usually generates hard computational problems for Wide and Deep [6]. Although a bit of out-of-dated, big data clusters and unsatisfactory results for shallow models are still widely used in small customers. In this paper, we propose a new companies and projects where agility, usability and algorithm solving the problem in the framework of matrix factorization. We model the data skewness efficiency far outweighs boost of performance which factors in the theoretic modeling of the approach is only economically visible for huge datasets. It is with easy to interpret and easy to implement well known since the invention of the first shallow formulas. We prove in experiments our method model, that data skewness and sparsity poses generates comparably favorite results with popular serious challenges for recommender system recommender system algorithms such as Learning performance. The setbacks are two folds: data to Rank, Alternating Least Squares and Deep Matrix skewness causes problems that need special Factorization.

Have you ever caught yourself thinking that no one understands you better than Netflix or YouTube? They just seem to get what you want and are always ready to deliver. The explanation for this impressive personalization lies in advanced data analytics mechanisms. The privacy concerns around big data are not empty words – BI does help business owners monetize your desires. When one thinks of the entertainment industry, the things that come to mind first are movies, theaters, concert venues, and sporting events.

Joe Biden has defeated President Donald Trump in the 2020 presidential election and will become the 46th president of the United States. Although Trump has not conceded, Biden has been named the president-elect by multiple outlets, including AP News. The decision to name the 77-year-old the president-elect sent Twitter into a frenzy, which resulted in Biden's supporters using Alexa, Amazon's virtual assistant, to taunt Trump and celebrate the democrat's victory. On Saturday, "Alexa" began trending on Twitter as people began sharing the songs they wanted to play to celebrate the president-elect and say goodbye to Trump. In the song, Meek Mill raps, "See my dreams unfold, nightmares come true It was time to marry the game and I said, 'Yeah, I do' If you want it you gotta see it with a clear-eyed view."

Artificial Intelligence (AI) has been a leading factor in numerous eye-opening marketing experiments in the past few years. Studies show that 80% of B2B marketers believe that AI will revolutionize marketing in the coming years. AI can emulate the human mind's capacity while making more precise business decisions. Using big data and intelligent machine learning, AI can help B2B marketers make more informed decisions and put their money into the right places. Let's look at how AI is changing the marketing world and leading to massive customer engagement and revenue improvements.

Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021. Companies have swiftly started leveraging the potential of AI.

Artificial Intelligence, once a buzzword in the digital world, has become a part of our everyday life. From Google Assistant, Siri, Alexa to Uber and Ola, several AI-enabled services are available today that make our lives easier. The ongoing pandemic has undoubtedly impacted business models but it didn't wane the impact AI has on our lives and businesses. On the contrary, it has become evident that Artificial Intelligence, with its self-teaching and learning algorithms, will play an essential role in transforming businesses in 2021.Companies have swiftly started leveraging the potential of AI. Companies like Amazon, Microsoft, and Google have grown immensely due to the incorporation of AI for forecasting, adapting to changing market conditions and generating profit.

Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.

The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism. We first show in theory that applying supervised learning to detect user preferences may end up with inconsistent results in the absence of exposure information. The counterfactual propensity-weighting approach from causal inference can account for the exposure mechanism; nevertheless, the partial-observation nature of the feedback data can cause identifiability issues. We propose a principled solution by introducing a minimax empirical risk formulation. We show that the relaxation of the dual problem can be converted to an adversarial game between two recommendation models, where the opponent of the candidate model characterizes the underlying exposure mechanism. We provide learning bounds and conduct extensive simulation studies to illustrate and justify the proposed approach over a broad range of recommendation settings, which shed insights on the various benefits of the proposed approach.

Unlike humans, conversational artificial intelligence (AI), most commonly deployed today via chatbots, are "up" 100% of the time. Beyond chatbots, automated voice response systems (as annoying as they may still be) and virtual voice assistants all utilize conversational AI to power human-to-machine dialog. Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).