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.
Hybrid approach combines user feedback and machine learning for predicting user satisfaction.
Study on electronic banking satisfaction in Nigeria.
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limit…
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low …
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfa…
A new online learning problem, CAB, tackles matching platforms to maximize user satisfaction.
Paper presents a reinforcement learning framework for personalized music playlist generation.
LIMEADE improves AI advice for opaque models, enhancing accuracy and user satisfaction.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
UCFE benchmarks LLMs in financial tasks with human feedback.
An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video strea…
New algorithm optimizes for long-term user satisfaction in delayed reward settings.
A new method for precise user targeting in advertising using hyperbolic manifold learning.
New algorithm optimizes long-term user satisfaction in recommendation systems.
We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks. Most real-world social networks are multi-graphs, where different kinds of relationships (e.g. chat, friendship) are possible between a pair of users. Existing network embedding techniques do not leve…
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve th…
Improved image synthesis with user scribbles and text prompts.
Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.
The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversat…
DPBD simplifies labeling functions through interactive demonstrations.
A deep reinforcement learning approach for slate re-ranking in e-commerce.
Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand…
In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …
We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a W…
Study shows non-systematic bias in customer satisfaction surveys limits data value.
Paper proposes efficient UAV placement for aerial base stations.
We reformulate data-dependent constraints to ensure they are always met with high probability.
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minor…
Enhanced neural network framework improves constraint satisfaction with topological conditioning.
E-scores assess LLM outputs for correctness, addressing p-hacking issues.
Polestar optimizes public transportation routes for efficiency and user satisfaction.
Improved text generation with constraints using discrete auto-regressive biasing.
A considerable part of the success experienced by Voice-controlled virtual assistants (VVA) is due to the emotional and personalized experience they deliver, with humor being a key component in providing an engaging interaction. In this paper we describe methods used to improve the joke skill of a VVA through personali…
We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algori…
New algorithm models satiation in recommender systems.
Analyzes retail trends from sales, search, and reviews.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
This study analyzes app reviews to understand students' behavior in the app market.
As known, attribute selection is a method that is used before the classification of data mining. In this study, a new data set has been created by using attributes expressing overall satisfaction in Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking search method using…
We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise betwee…
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives…
Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by generative adversarial and transfer learning that allows users to listen to a selected target singer singing any song. SCM-GAN first separates …
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these au…
GACEM optimizes complex multi-modal problems using neural networks.
AI classifies tourist events for better service.
A new ML method teaches constraints directly to models.