A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a model to optimize feedback for content creators on social media.
Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this pa…
Enhanced word embedding creates new consumer-friendly health terms.
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the …
Study uses geometric algebra to analyze credit cycles, revealing dangerous feedback loops.
The paper solves a consumption-investment problem with state-dependent lower bounds.
This paper proposes a percolation-based model of new-product diffusion in the spirit of Solomon et al. (2000) and Goldenberg et al. (2000). A consumer buys the new product if she has formed her individual valuation of the product (reservation price) and if this valuation is greater or equal than the price of the produc…
New method provides fine-grained feedback on interactive student programs.
Online reviews predict long-term stock returns.
Dual active learning improves RLHF by selecting optimal conversations and teachers.
The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…
We propose a continuum model for the description of buyer and seller dynamics in an Internet market. The relevant variables are the research effort of buyers and the sellers' reputation building process. We show that, if a commercial web-site gives consumers the possibility to rate credibly sellers they bargained with,…
The paper optimizes exceptions in a statistical production system using machine learning.
This communication is based on an original approach linking economical factors to technical and methodological ones. This work is applied to the decision process for mix production. This approach is relevant for costing driving systems. The main interesting point is that the quotation factors (linked to time indicators…
Convolutional neural networks improve surgical skill evaluation.
New algorithm reduces regret and constraint violation in constrained bandit problems.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different…
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm …
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to …
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
The aim of this work is to address the description of hyperinflation regimes in economy. The spirals of hyperinflation developed in Brazil, Israel, and Nicaragua are revisited. This new analysis of data indicates that the episodes occurred in Brazil and Nicaragua can be understood within the frame of the model availabl…
Neural nets predict user attention from mouse movements.
A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
Analyzes new economic paradigm for non-independent consumer choices.
Method detects multi-timescale consumer spending patterns from receipts.
Study uses EEG and ML to predict movie ratings with 72% accuracy.
Detects systematic anomalies in consumer complaints using NLP.
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…
Study finds consumers are more price-sensitive before livestreams than after.
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, auto…
An extension of the nonlinear feedback (NLF) formalism to describe regimes of hyper- and high-inflation in economy is proposed in the present work. In the NLF model the consumer price index (CPI) exhibits a finite time singularity of the type , with , predicting a blow up of the economy at a…
A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
The paper explores fairness, welfare, and equity in personalized pricing across various applications.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
Model forecasts natural gas consumption with Fourier series and feedback.
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…
New method learns credit prices offline without interaction.
Paper proposes a method to estimate consumer valuations from bundle sales data.
Study improves retail demand forecasting by integrating macroeconomic data.
NeuCredit model predicts consumer credit risk using e-commerce data.
The paper improves consumer preference modeling by considering multiple product categories.
Paper uses GANs to simulate consumer transactions with SKU constraints.
We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchas…
System designs for analyzing and pricing non-performing consumer credit portfolios.
GBS uses machine learning to design products based on consumer preferences.
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited b…
Paper explores a consumer-friendly approach to explain machine learning decisions.