The modern internet is powered by recommendation algorithms. These systems track your online consumption and use that data to suggest the next piece of content for you to absorb. Their goal is to keep users on a platform by presenting them with things they'll spend more time engaging with. Trouble is, those link chains can lead to some weird places, occasionally taking users down dark internet rabbit holes or showing harmful content. Lawmakers and researchers have criticized recommendation systems before, but these methods are under renewed scrutiny now that Google and Twitter are going before the US Supreme Court to defend their algorithmic practices.

Some say the metaverse is nothing but marketing hype, while others insist it will transform society. I fall into the latter camp, but I'm not talking about cartoon worlds filled with avatars like many are pitching. Instead, I believe the true metaverse – the one that will change society -- will be an augmentation layer on the real world, and within 10 years it will be the foundation of our lives, impacting everything from shopping and socializing to business and education. I also believe that a corporate-controlled metaverse is dangerous to society and requires aggressive regulation. That's because the platform providers will be able to manipulate consumers in ways that will make social media seem quaint.

Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Systems area includes a large number of subfields in which accuracy and beyond accuracy quality measures are continuously improved. To feed this research variety, it is necessary and convenient to reinforce the existing datasets with synthetic ones. This paper proposes a Generative Adversarial Network (GAN)-based method to generate collaborative filtering datasets in a parameterized way, by selecting their preferred number of users, items, samples, and stochastic variability. This parameterization cannot be made using regular GANs. Our GAN model is fed with dense, short, and continuous embedding representations of items and users, instead of sparse, large, and discrete vectors, to make an accurate and quick learning, compared to the traditional approach based on large and sparse input vectors. The proposed architecture includes a DeepMF model to extract the dense user and item embeddings, as well as a clustering process to convert from the dense GAN generated samples to the discrete and sparse ones, necessary to create each required synthetic dataset. The results of three different source datasets show adequate distributions and expected quality values and evolutions on the generated datasets compared to the source ones. Synthetic datasets and source codes are available to researchers.

Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.

Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and inference latency proportional to the number of models, which remains the bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy. Through an empirical study, we find that the efficacy of distillation severely drops when transferring knowledge from heterogeneous teachers. Nevertheless, we show that an important signal to ease the difficulty can be obtained from the teacher's training trajectory. This paper proposes a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from the teachers' trajectories. To provide guidance according to the student's learning state, HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Our comprehensive experiments show that HetComp significantly improves the distillation quality and the generalization of the student model.

Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.

Welcome to the first edition of a new article series called "The Kaggle Blueprints", where we will analyze Kaggle competitions' top solutions for lessons we can apply to our own data science projects. This first edition will review the techniques and approaches from the "OTTO -- Multi-Objective Recommender System" competition, which ended at the end of January, 2023. The goal of the "OTTO -- Multi-Objective Recommender System" competition was to build a multi-objective recommender system (RecSys) based on a large dataset of implicit user data. One of the main challenges of this competition was the large number of items to choose from. Feeding all of the available information into a complex model would require the availability of extensive amounts of computational resources.

In today's market, automation in banking has become a crucial factor for banks to remain competitive. Banks are leveraging automation to offer personalised services to customers, reduce operational costs, and improve the speed and accuracy of their processes. Automation technologies like Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) are being used to automate repetitive and time-consuming tasks such as data entry, account opening, loan processing, and customer service. One of the significant benefits of automation in banking is improved customer experience. Banks are using chatbots and virtual assistants to provide 24/7 customer service, allowing customers to get instant assistance without having to wait for a human representative.

Boris Kontsevoi is a technology executive, President and CEO of Intetics Inc., a global software engineering and data processing company. Whether it's Alexa, Bixby, Siri, Google or Cortana, you are probably using conversational AI from your phone to your car. Conversational AI is a special branch of artificial intelligence that simply enables computers to understand human speech and reply by voice. One of the most critical impacts of this is that the global GDP is expected to grow by $15.7 trillion by 2030 thanks to AI. This revolutionary technology combines three components: machine learning, automatic speech recognition and natural language processing (NLP). These days, conversational AI looks like a fulfilled dream: you simply ask a question and get a talking answer.

Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of formal method experts as a platform for construction and (formal) verification of proofs. The complexity of the proofs in conjunction with the level of expertise typically required for the process to succeed can often hinder the adoption of ITPs. A recent strain of work has investigated methods to incorporate machine learning models trained on ITP user activity traces as a viable path towards full automation. While a valuable line of investigation, many problems still require human supervision to be completed fully, thus applying learning methods to assist the user with useful recommendations can prove more fruitful. Following the vein of user assistance, we introduce CoProver, a proof recommender system based on transformers, capable of learning from past actions during proof construction, all while exploring knowledge stored in the ITP concerning previous proofs. CoProver employs a neurally learnt sequence-based encoding of sequents, capturing long distance relationships between terms and hidden cues therein. We couple CoProver with the Prototype Verification System (PVS) and evaluate its performance on two key areas, namely: (1) Next Proof Action Recommendation, and (2) Relevant Lemma Retrieval given a library of theories. We evaluate CoProver on a series of well-established metrics originating from the recommender system and information retrieval communities, respectively. We show that CoProver successfully outperforms prior state of the art applied to recommendation in the domain. We conclude by discussing future directions viable for CoProver (and similar approaches) such as argument prediction, proof summarization, and more.