The Application of Affective Measures in Text-based Emotion Aware Recommender Systems

• URL: http://arxiv.org/abs/2305.04796v1
• Date: Thu, 4 May 2023 13:26:49 GMT
• Title: The Application of Affective Measures in Text-based Emotion Aware Recommender Systems
• Authors: John Kalung Leung, Igor Griva, William G. Kennedy, Jason M. Kinser, Sohyun Park, and Seo Young Lee
• Abstract summary: This paper introduces a method that detects affective features in subjective passages using the Generative Pre-trained Transformer Technology. The paper advocates for a separation of responsibility approach where users protect their emotional profile data while EARS service providers refrain from retaining or storing it.
• Score: 1.1942757095667953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an innovative approach to address the problems researchers face in Emotion Aware Recommender Systems (EARS): the difficulty and cumbersome collecting voluminously good quality emotion-tagged datasets and an effective way to protect users' emotional data privacy. Without enough good-quality emotion-tagged datasets, researchers cannot conduct repeatable affective computing research in EARS that generates personalized recommendations based on users' emotional preferences. Similarly, if we fail to fully protect users' emotional data privacy, users could resist engaging with EARS services. This paper introduced a method that detects affective features in subjective passages using the Generative Pre-trained Transformer Technology, forming the basis of the Affective Index and Affective Index Indicator (AII). Eliminate the need for users to build an affective feature detection mechanism. The paper advocates for a separation of responsibility approach where users protect their emotional profile data while EARS service providers refrain from retaining or storing it. Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy. This paper offers a solution to the subjectivity and variability of emotions, data privacy concerns, and evaluation metrics and benchmarks, paving the way for future EARS research.

Related papers

• EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation [13.048477440429195]
  This paper presents the EEG Emotion Copilot, a system leveraging a lightweight large language model (LLM) operating in a local setting. The system is designed to first recognize emotional states directly from EEG signals, subsequently generate personalized diagnostic and treatment suggestions. Privacy concerns are also addressed, with a focus on ethical data collection, processing, and the protection of users' personal information.
  arXiv  Detail & Related papers  (2024-09-30T19:15:05Z)
• Towards Empathetic Conversational Recommender Systems [77.53167131692]
  We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.
  arXiv  Detail & Related papers  (2024-08-30T15:43:07Z)
• Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation [0.39321523855648755]
  We propose an approach to enhance Interpersonal Emotion Regulation on online platforms through content recommendation. The proposed recommendation system is expected to blend system-initiated and user-initiated emotion regulation. The study collects 37.5K instances of user posts and interactions on Reddit over a year to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system.
  arXiv  Detail & Related papers  (2024-08-05T10:27:28Z)
• NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human [55.20137833039499]
  We suggest sanitizing sensitive text using two common strategies used by humans. We curate the first corpus, coined NAP2, through both crowdsourcing and the use of large language models.
  arXiv  Detail & Related papers  (2024-06-06T05:07:44Z)
• Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots [0.0]
  This paper proposes an approach to enhance therapy chatbots with auditory perception, enabling them to understand users' feelings and provide human-like empathy. The proposed method incorporates speech emotion recognition (SER) techniques using CNN models and the ShEMO dataset. To provide a more immersive and empathetic user experience, a text-to-speech model called GlowTTS is integrated.
  arXiv  Detail & Related papers  (2023-11-18T16:35:55Z)
• Unlearning Protected User Attributes in Recommendations with Adversarial Training [10.268369743620159]
  Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users. These encoded biases can influence the decision of a recommendation system towards further separation of the contents provided to various demographic subgroups. In this work, we investigate the possibility and challenges of removing specific protected information of users from the learned interaction representations of a RS algorithm.
  arXiv  Detail & Related papers  (2022-06-09T13:36:28Z)
• MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation [64.37111498077866]
  We propose a novel model for emotional support conversation. It infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-03-25T10:32:04Z)
• Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings [56.93025161787725]
  Federated learning (FL) is a distributed machine learning paradigm that coordinates clients to train a model collaboratively without sharing local data. We propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters. We show that the attribute inference attack is achievable for SER systems trained using FL.
  arXiv  Detail & Related papers  (2021-12-26T16:50:42Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Learning Emotional-Blinded Face Representations [77.7653702071127]
  We propose two face representations that are blind to facial expressions associated to emotional responses. This work is motivated by new international regulations for personal data protection.
  arXiv  Detail & Related papers  (2020-09-18T09:24:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.