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.
Paper proposes TPathMine model for more accurate user attribute prediction.
Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.
Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional sta…
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
Paper proposes a framework to protect user anonymity in emotion recognition.
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the laten…
Emotional content is a crucial ingredient in user-generated videos. However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis. In this paper, we propose a new neural approach, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion …
Enhances privacy in multimodal emotion recognition models.
Online social platforms have been the battlefield of users with different emotions and attitudes toward each other in recent years. While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techn…
Bayesian optimization agent learns user preferences from pairwise comparisons.
End-to-end model detects emotions and predicts Facebook reactions.
We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive st…
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.
New study shows personalized content recommendations can lead to polarization of user preferences.
New method adapts to user preferences dynamically, improving recommendation models.
Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, requires user feedback on the robot's behavior. In this work, we propose a novel approach to leverage the user's brain signals as a feedback modality in order to decode the judgment of rob…
Model user preferences for conversational LLMs using weak rewards.
Bardo Composer generates tabletop RPG music based on player speech.
Study creates web interface to elicit user-preferred metrics.
Estimates users' preference for a site over others using engagement data.
DPA aligns LLMs with multi-objective rewards for diverse user preferences.
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
System detects financial forecasts in tweets, achieving high precision.
FSPO optimizes synthetic preferences for LLM personalization.
Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured d…
Model learns metrics and preferences from user comparisons.
Generates music with video emotion using deep neural networks.
This research improves EEG-based MI-BCI systems to be more resilient to emotional arousal.
A-GPS learns to generate Pareto sets efficiently with user preferences.
This paper learns user preferences from comparisons using Mahalanobis metrics.
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user grou…
We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, wh…
In this paper, we investigate the impact of diverse user preference on learning under the stochastic multi-armed bandit (MAB) framework. We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms un…
Proposes auditing for envy-freeness in recommender systems to assess individual preferences.
Proposes CF-SFL to improve sparse data recommendation.
Paper proposes a two-stage ranking for personalized TV recommendations.
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top- items from the estimated preference order is recommended to the end user in the decreasing …
This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment ana…
While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providi…
PGRec improves recommendation by modeling user-item preferences as a graph and embedding it for better predictions.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user prefe…
Network-based models predict user preferences for items like movies and research articles.
IArxiv sorts daily papers by user preference using LDA.
Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference in…
New MAB model incentivizes user arm-pulling with self-reinforcing preferences.
Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-b…