Related papers: Development of Choice Model for Brand Evaluation
Users on the internet usually require venues to provide better purchasing recommendations. This can be provided by a reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of…
Learning user preferences for products based on their past purchases or reviews is at the cornerstone of modern recommendation engines. One complication in this learning task is that some users are more likely to purchase products or review…
Understanding individual customers' sensitivities to prices, promotions, brands, and other marketing mix elements is fundamental to a wide swath of marketing problems. An important but understudied aspect of this problem is the dynamic…
Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given…
One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific,…
With the increasing deployment of machine learning models in many socially sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from…
Most online markets establish reputation systems to assist building trust between sellers and buyers. Sellers' reputations not only provide guidelines for buyers but may also inform sellers their optimal pricing strategy. In this research,…
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong…
This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in…
The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the…
An important question in economics is how people choose between different payments in the future. The classical normative model predicts that a decision maker discounts a later payment relative to an earlier one by an exponential function…
This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision making, both for new customers and manufacturers. Such a rating system gives a more…
With the widespread use of mobile phones, users can share their location anytime, anywhere, as a form of check-in data. These data reflect user preferences. Furthermore, the preference rules for different users vary. How to discover a…
A new class of general exponential ranking models is introduced which we label angle-based models for ranking data. A consensus score vector is assumed, which assigns scores to a set of items, where the scores reflect a consensus view of…
School choice mechanism designers use discrete choice models to understand and predict families' preferences. The most widely-used choice model, the multinomial logit (MNL), is linear in school and/or household attributes. While the model…
A consumer Behaviour model is considered in the context of a network of interacting individuals in an energy market. We propose and analyse a simple dynamical model of an ensemble of coupled active elements mimicking consumers' Behaviour,…
Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction…
Consumers discover their preferences through experience, yet the sequence and composition of those experiences are often designed by firms, digital platforms, or policymakers. We introduce a ``data-design'' framework for preference…
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes…
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key…