Pub Date : 2024-06-22DOI: 10.1007/s11257-024-09403-3
Martina Di Bratto, Antonio Origlia, Maria Di Maro, Sabrina Mennella
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.
{"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":"<p>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 <i>deliberation</i> dialogue in which participants share their specific <i>beliefs</i> in the respective representations of the <i>common ground</i>, 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</p>","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}
Pub Date : 2024-06-21DOI: 10.1007/s11257-024-09397-y
Stefano Valtolina, Ricardo Anibal Matamoros, Francesco Epifania
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.
近年来,我们看到电子学习平台大量涌现。电子学习平台可以让教师以更有效、更省时的方式创建数字化课程,但也有一些缺陷阻碍了它们的实际成功。其中一个主要问题是,由于可供选择的开放式学习材料太多,教师很难准确地找到并组合符合其特定需求的学习材料。本文提出了一种创建以学习对象(LOs)为主要元素的数字课程的新策略。这一想法包括使用智能聊天机器人协助教师开展活动。该聊天机器人使用 RASA 技术进行定义,根据教师的个人资料和需求,询问有关教师必须创建的课程的信息。聊天机器人会建议最佳的学习目标,并根据其先决条件和结果来组合它们。基于聊天机器人的推荐系统通过 BERT(一种基于 Transformers 的机器学习模型)提供建议,以确定输入数据与 LO 元数据之间的语义相似性。此外,聊天机器人还建议如何将学习成果组合成最终的学习路径。最后,本文介绍了教师在创建数字课程时进行测试的一些初步结果。
{"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":"<p>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.</p>","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}
Pub Date : 2024-06-21DOI: 10.1007/s11257-024-09401-5
Maarten van der Velde, Florian Sense, Jelmer P. Borst, Hedderik van Rijn
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.
{"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":"<p>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.</p>","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}
Pub Date : 2024-06-06DOI: 10.1007/s11257-024-09398-x
Siamak Farshidi, Kiyan Rezaee, Sara Mazaheri, Amir Hossein Rahimi, Ali Dadashzadeh, Morteza Ziabakhsh, S. Eskandari, Slinger Jansen
{"title":"Understanding user intent modeling for conversational recommender systems: a systematic literature review","authors":"Siamak Farshidi, Kiyan Rezaee, Sara Mazaheri, Amir Hossein Rahimi, Ali Dadashzadeh, Morteza Ziabakhsh, S. Eskandari, Slinger Jansen","doi":"10.1007/s11257-024-09398-x","DOIUrl":"https://doi.org/10.1007/s11257-024-09398-x","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378209","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}
Pub Date : 2024-06-06DOI: 10.1007/s11257-024-09400-6
Luis M. de Campos, J. M. Fernández-Luna, J. Huete
{"title":"An explainable content-based approach for recommender systems: a case study in journal recommendation for paper submission","authors":"Luis M. de Campos, J. M. Fernández-Luna, J. Huete","doi":"10.1007/s11257-024-09400-6","DOIUrl":"https://doi.org/10.1007/s11257-024-09400-6","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381851","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}
Pub Date : 2024-04-17DOI: 10.1007/s11257-024-09393-2
Miguel Portaz, Alberto Corbi, Alberto Casas-Ortiz, Olga C. Santos
This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.
{"title":"Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skills","authors":"Miguel Portaz, Alberto Corbi, Alberto Casas-Ortiz, Olga C. Santos","doi":"10.1007/s11257-024-09393-2","DOIUrl":"https://doi.org/10.1007/s11257-024-09393-2","url":null,"abstract":"<p>This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.\u0000</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616172","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}
Pub Date : 2024-04-06DOI: 10.1007/s11257-024-09396-z
Radek Pelánek, Tomáš Effenberger, Petr Jarušek
Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.
{"title":"Personalized recommendations for learning activities in online environments: a modular rule-based approach","authors":"Radek Pelánek, Tomáš Effenberger, Petr Jarušek","doi":"10.1007/s11257-024-09396-z","DOIUrl":"https://doi.org/10.1007/s11257-024-09396-z","url":null,"abstract":"<p>Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.\u0000</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591553","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}
Pub Date : 2024-03-28DOI: 10.1007/s11257-024-09392-3
Oliver W. Klaproth, Emmanuelle Dietz, Juliane Pawlitzki, L. R. Krol, T. O. Zander, Nele Russwinkel
{"title":"Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data","authors":"Oliver W. Klaproth, Emmanuelle Dietz, Juliane Pawlitzki, L. R. Krol, T. O. Zander, Nele Russwinkel","doi":"10.1007/s11257-024-09392-3","DOIUrl":"https://doi.org/10.1007/s11257-024-09392-3","url":null,"abstract":"","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":"140369568","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}
Pub Date : 2024-03-28DOI: 10.1007/s11257-024-09395-0
Albert Saiapin, Gleb Balitskiy, Daniel Bershatsky, Aleksandr Katrutsa, Evgeny Frolov, Alexey Frolov, Ivan Oseledets, Vitaliy Kharin
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.
{"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":"<p>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.</p>","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}