User Modeling and User-Adapted Interaction最新文献_第2页

Linguistics-based dialogue simulations to evaluate argumentative conversational recommender systems 基于语言学的对话模拟，评估论证式对话推荐系统

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-22 DOI: 10.1007/s11257-024-09403-3

Martina Di Bratto, Antonio Origlia, Maria Di Maro, Sabrina Mennella

{"title":"Linguistics-based dialogue simulations to evaluate argumentative conversational recommender systems","authors":"Martina Di Bratto, Antonio Origlia, Maria Di Maro, Sabrina Mennella","doi":"10.1007/s11257-024-09403-3","DOIUrl":"https://doi.org/10.1007/s11257-024-09403-3","url":null,"abstract":"Conversational recommender systems aim at recommending the most relevant information for users based on textual or spoken dialogues, through which users can communicate their preferences to the system more efficiently. Argumentative conversational recommender systems represent a kind of deliberation dialogue in which participants share their specific beliefs in the respective representations of the common ground, to act towards a common goal. The goal of such systems is to present appropriate supporting arguments to their recommendations to show the interlocutor that a specific item corresponds to their manifested interests. Here, we present a cross-disciplinary argumentation-based conversational recommender model based on cognitive pragmatics. We also present a dialogue simulator to investigate the quality of the theoretical background. We produced a set of synthetic dialogues based on a computational model implementing the linguistic theory and we collected human evaluations about the plausibility and efficiency of these dialogues. Our results show that the synthetic dialogues obtain high scores concerning their naturalness and the selection of the supporting arguments.\u0000","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of a conversational recommender system in education 设计教育对话推荐系统

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-21 DOI: 10.1007/s11257-024-09397-y

Stefano Valtolina, Ricardo Anibal Matamoros, Francesco Epifania

{"title":"Design of a conversational recommender system in education","authors":"Stefano Valtolina, Ricardo Anibal Matamoros, Francesco Epifania","doi":"10.1007/s11257-024-09397-y","DOIUrl":"https://doi.org/10.1007/s11257-024-09397-y","url":null,"abstract":"In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large-scale evaluation of cold-start mitigation in adaptive fact learning: Knowing “what” matters more than knowing “who” 大规模评估适应性事实学习中的冷启动缓解措施：知道 "什么 "比知道 "谁 "更重要

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-21 DOI: 10.1007/s11257-024-09401-5

Maarten van der Velde, Florian Sense, Jelmer P. Borst, Hedderik van Rijn

{"title":"Large-scale evaluation of cold-start mitigation in adaptive fact learning: Knowing “what” matters more than knowing “who”","authors":"Maarten van der Velde, Florian Sense, Jelmer P. Borst, Hedderik van Rijn","doi":"10.1007/s11257-024-09401-5","DOIUrl":"https://doi.org/10.1007/s11257-024-09401-5","url":null,"abstract":"Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this “cold-start” problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners’ memory performance than using default values. We found that predictions based on the difficulty of the fact (“what”) generally outperformed predictions based on the ability of the learner (“who”), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving selection diversity using hybrid graph-based news recommenders 利用基于图谱的混合新闻推荐器提高选择多样性

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-12 DOI: 10.1007/s11257-024-09399-w

Stefaan Vercoutere, Glen Joris, Toon de Pessemier, Luc Martens

引用次数: 0

Understanding user intent modeling for conversational recommender systems: a systematic literature review 了解对话式推荐系统的用户意图建模：系统性文献综述

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-06 DOI: 10.1007/s11257-024-09398-x

Siamak Farshidi, Kiyan Rezaee, Sara Mazaheri, Amir Hossein Rahimi, Ali Dadashzadeh, Morteza Ziabakhsh, S. Eskandari, Slinger Jansen

引用次数: 0

An explainable content-based approach for recommender systems: a case study in journal recommendation for paper submission 基于内容的可解释推荐系统方法：期刊论文投稿推荐案例研究

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-06-06 DOI: 10.1007/s11257-024-09400-6

Luis M. de Campos, J. M. Fernández-Luna, J. Huete

引用次数: 0

Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skills 探索惯性传感器数据的原始数据转换，为用户学习心理运动技能时的专业知识建模

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-04-17 DOI: 10.1007/s11257-024-09393-2

Miguel Portaz, Alberto Corbi, Alberto Casas-Ortiz, Olga C. Santos

引用次数: 0

Personalized recommendations for learning activities in online environments: a modular rule-based approach 在线环境中学习活动的个性化推荐：基于模块规则的方法

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-04-06 DOI: 10.1007/s11257-024-09396-z

Radek Pelánek, Tomáš Effenberger, Petr Jarušek

引用次数: 0

Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data 利用基于实例的学习建立预测模型：利用被动生物识别（BCI）和眼动跟踪数据应用于航空自动化惊喜

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-03-28 DOI: 10.1007/s11257-024-09392-3

Oliver W. Klaproth, Emmanuelle Dietz, Juliane Pawlitzki, L. R. Krol, T. O. Zander, Nele Russwinkel

引用次数: 0

Federated privacy-preserving collaborative filtering for on-device next app prediction 针对设备上的下一个应用程序预测的联合隐私保护协同过滤技术

IF 3.6 3区计算机科学

User Modeling and User-Adapted Interaction Pub Date : 2024-03-28 DOI: 10.1007/s11257-024-09395-0

Albert Saiapin, Gleb Balitskiy, Daniel Bershatsky, Aleksandr Katrutsa, Evgeny Frolov, Alexey Frolov, Ivan Oseledets, Vitaliy Kharin

{"title":"Federated privacy-preserving collaborative filtering for on-device next app prediction","authors":"Albert Saiapin, Gleb Balitskiy, Daniel Bershatsky, Aleksandr Katrutsa, Evgeny Frolov, Alexey Frolov, Ivan Oseledets, Vitaliy Kharin","doi":"10.1007/s11257-024-09395-0","DOIUrl":"https://doi.org/10.1007/s11257-024-09395-0","url":null,"abstract":"In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices, and the transmission of users’ data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of our approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model, we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0