1,721,063 research outputs found
The Infuence of User's Emotions in Recommender Systems for Decision Making Processes
No full textThe decision making process is a very hard task to face, because a lot of external elements could influence the final decision taken.
The paper will present the influences of emotions and personality in this task and will propose a recommender system able to take them in accounts during the recommendation process
Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality
No full textThe influence of emotions in decision making is a popular research topic in psychology and cognitive studies. A person facing a choosing problem has to consider different solutions and take a decision. During this process several elements influence the reasoning, some of them are rational, others are irrational, such as emotions. Recommender Systems can be used to support decision making by narrowing the space of options. Typically they do not consider irrational elements during the computational process, but recent studies show that accuracy of suggestions improves whether user’s emotional state is included in the recommendation process. In this paper we propose the idea of defining a framework for an Emotion-Aware Recommender System. The user emotions will be formalized in an affective user profile which can act as an emotional computational model. The Recommender System will use the affective profile integrated with case base reasoning to compute recommendations
A Framework for Emotion-aware Recommender Systems supporting Decision Making
No full textEmotions influence ever yday decisions. When people make decisions about movies to watch, songs to listen or even about more serious issues such as health, they perform a cognitive process that estimates which of various alternative choices would yield the most positive consequences. Indeed, this process in not totally rational because it is influenced, directly or in a subtle way by personality traits and emotions. In this paper we propose the idea of defining an affective user profile , which can act as a computational model of personality and emotions, included in a g eneral, affective-aware , recommendation framework
Introduction to the Special Issue on Natural Language for Artificial Intelligence in the Era of LLMs
No full textThe rapid advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) in recent years. Transformer-based models such as GPT-3 and BERT have demonstrated remarkable capabilities in modeling and generating human-like text. These models have not only redefined the potential of AI systems but also revolutionized applications across a broad spectrum, including machine translation, sentiment analysis, question answering, and beyond. While the era of LLMs has significantly expanded the horizons of AI, it has also presented critical challenges in effectively and responsibly harnessing their capabilities
Comparing Human Pose Estimation through deep learning approaches: An overview
No full textIn the everyday IoT ecosystem, many devices and systems are interconnected in an intelligent living environment to create a comfortable and efficient living space. In this scenario, approaches based on automatic recognition of actions and events can support fully autonomous digital assistants and personalized services. A pivotal component in this domain is “Human Pose Estimation”, which plays a critical role in action recognition for a wide range of applications, including home automation, healthcare, safety, and security. These systems are designed to detect human actions and deliver customized real-time responses and support. Selecting an appropriate technique for Human Pose Estimation is crucial to enhancing these systems for various applications. This choice hinges on the specific environment and can be categorized on the basis of whether the technique is designed for images or videos, single-person or multi-person scenarios, and monocular or multiview inputs. A comprehensive overview of recent research outcomes is essential to showcase the evolution of the research area, along with its underlying principles and varied application domains. Key benchmarks across these techniques are suitable and provide valuable insights into their performance. Hence, the paper summarizes these benchmarks, offering a comparative analysis of the techniques. As research in this field continues to evolve, it is critical for researchers to stay up to date with the latest developments and methodologies to promote further innovations in the field of pose estimation research. Therefore, this comprehensive overview presents a thorough examination of the subject matter, encompassing all pertinent details. Its objective is to equip researchers with the knowledge and resources necessary to investigate the topic and effectively retrieve all relevant information necessary for their investigations
SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networks
No full textEmotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at https://github.com/marcopoli/EmoContext201
An emotion-driven approach for aspect-based opinion mining
No full textThe remarkable ability to understand the opinion of a user about a specific topic of discussion allows intelligent systems to provide more specific and personalized suggestions especially when no other information is available. The strategies for opinion mining, also known as sentiment analysis, are in last years topic of in-depth studies. In this work, we present an approach of text mining for detecting the topic of discussion for textual contents and the emotion that the writer feels while writing it. Conversely to the classic strategies of sentiment analysis, we enrich the standard polarity prediction task with more fine-grained information about user’s emotion. By using this information, the final behavior of the personalized system could be designed by taking into account the view about the topic of the specific user. For performing this task, we adopted a hybrid approach which uses both lexicons and semantic representation of sentences for the operation of aspect classification. Training data for the aspects detection module have been extracted from already categorized last year world news. The emotional labeling approach is, instead, based on the posts left by users on Facebook, which have been annotated using the emoticon encountered. The evaluation has been conducted on a dataset of tweets opportunely collected using hash-tags which refer both to the topic of discussion and the emotional opinion
Hansel: Italian hate speech detection through ensemble learning and deep neural networks
No full textThe detection of hate speeches, over social media and online forums, is a relevant task for the research area of natural language processing. This interest is motivated by the complexity of the task and the social impact of its use in real scenarios. The task solution proposed in this work is based on an ensemble of three classification strategies, mediated by a majority vote algorithm: Support Vector Machine (Hearst et al., 1998) (SVM with RBF kernel), Random Forest (Breiman, 2001), Deep Multilayer Perceptron (Kolmogorov, 1992) (MLP). Each classifier has been tuned using a greedy strategy of hyper-parameters optimization over the”F1” score calculated on a 5-fold random subdivision of the training set. Each sentence has been pre-processed to transform it into word embeddings and TF-IDF bag of words. The results obtained on the cross-validation over the training sets have shown an F1 value of 0.8034 for Facebook sentences and 0.7102 for Twitter. The code of the system proposed can be downloaded from GitHub: https: //github.com/marcopoli/ haspeede_hate_detect
Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist Continuation
No full textJARVIS: Adaptive Dual-Hemisphere Architectures For Personalized Large Agentic Models
No full textIn this work, we propose JARVIS. It aims to provide LLMs with a stronger degree of personalization via a two-hemisphere architecture inspired by the biological organization of the human brain, following a Large Agentic Model (LAM) architecture. The subjective hemisphere operates by dynamically modeling the user's preferences and iteratively optimizing its behaviors, through a training phase grounded on LoRA (Low-Rank Adaptation), DPO (Proximal Policy Optimization), human feedback, and synthetic data ("digital dreams"). Conversely, the objective hemisphere serves a rational-like role, reducing hallucination and the chances of getting dangerous misinformation using more structural approaches. In JARVIS, such hemispheres are ground on a dual-level memory capability. Short-Term memory keeps track of short-Term preferences, ensuring continuity in dialogues and long-Term user behaviors and interactions. Long-Term memory is gradually developed to collect all the possible user ground preferences, skills, and general behavioral routines. Unlike current state-of-The-Art approaches, JARVIS provides a personalized and context-Aware alternative, facilitating seamless and fluent interactions with the end-user
- …
