User Modeling and User-Adapted Interaction最新文献

{"title":"Toward joint utilization of absolute and relative bandit feedback for conversational recommendation","authors":"","doi":"10.1007/s11257-023-09388-5","DOIUrl":"https://doi.org/10.1007/s11257-023-09388-5","url":null,"abstract":"<h3>Abstract</h3> <p>Conversational recommendation has been a promising solution for recent recommenders to address the cold-start problem suffered by traditional recommender systems. To actively elicit users’ dynamically changing preferences, conversational recommender systems periodically query the users’ preferences on item attributes and collect conversational feedback. However, most existing conversational recommender systems only enable users to provide one type of feedback, either absolute or relative. In practice, absolute feedback can be biased and imprecise due to users’ varying rating criteria. Relative feedback, in the meanwhile, suffers from its hardship to reveal the absolute user attitudes. Hence, asking only one type of questions throughout the whole conversation may not efficiently elicit users’ preferences of high accuracy. Moreover, many existing conversational recommender systems only allow users to provide binary feedback, which can be noisy when users do not have a particular inclination. To address the above issues, we propose a generalized conversational recommendation framework, hybrid rating-comparison conversational recommender system. The system can seamlessly ask absolute and relative questions and incorporate both types of feedback with possible neutral responses. While it is promising to utilize different types of feedback together, it can be difficult to build a joint model incorporating them as they bear different interpretations of users’ preferences. To ensure relative feedback can be effectively leveraged, we first propose a bandit algorithm, RelativeConUCB. On the basis of it, we further propose a new bandit algorithm, <span>ArcUCB</span>, to utilize jointly absolute and relative feedback with possible neutral responses for preference elicitation. The experiments on both synthetic and real-world datasets validate the advantage of our proposed methods, in comparison with existing bandit algorithms in conversational recommender systems</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578606","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}

{"title":"Twenty-Five Years of Bayesian knowledge tracing: a systematic review","authors":"Šarić-Grgić Ines, Grubišić Ani, Gašpar Angelina","doi":"10.1007/s11257-023-09389-4","DOIUrl":"https://doi.org/10.1007/s11257-023-09389-4","url":null,"abstract":"<p>The quality of an artificial intelligence-based tutoring system is its ability to observe and interpret student behaviour to infer the preferences and needs of an individual student. The student model enables a comprehensive representation of student knowledge and affects the quality of the other intelligent tutoring system’s (ITS) components. The Bayesian knowledge tracing model (BKT) is one of the first machine learning-based and widely investigated student models due to its interpretability and ability to infer student knowledge. The past Twenty-five Years have seen increasingly rapid advances in the field, so this systematic review deals with the BKT model enhancements by using the PRISMA guidelines and a unique set of criteria, including 13 aspects of enhancements and computational methods. Also, the study reveals two types of evaluation approaches found in the literature, including the prediction of student answers and the ability to estimate knowledge mastery. Overall, the most frequently investigated enhancements extended the vanilla BKT model by including student characteristics and tutor interventions. The educational context-based enhancements of domain knowledge properties, question difficulty and architectural prior knowledge were also frequently investigated enhancements. The expectation–maximization algorithm practically became the standard in estimating BKT parameters. While the enhanced BKT models generally overperformed the vanilla model in predicting the student answer by using the measures such as RMSE (root mean square error), AUC–ROC (area under curve, receiver operating characteristics curve) and accuracy, only a few studies further investigated the systems’ estimations of knowledge mastery by correlating it to knowledge on post-tests. The most frequently used educational platforms included ITSs, Massive Open Online Courses (MOOCs) and simulated environments.</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"28 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578703","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}

{"title":"Adaptive user interfaces in systems targeting chronic disease: a systematic literature review","authors":"Wei Wang, Hourieh Khalajzadeh, John Grundy, Anuradha Madugalla, Jennifer McIntosh, Humphrey O. Obie","doi":"10.1007/s11257-023-09384-9","DOIUrl":"https://doi.org/10.1007/s11257-023-09384-9","url":null,"abstract":"<p>eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications. </p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"110 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715307","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}

{"title":"Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums","authors":"Laila Alrajhi, Ahmed Alamri, Filipe Dwan Pereira, Alexandra I. Cristea, Elaine H. T. Oliveira","doi":"10.1007/s11257-023-09381-y","DOIUrl":"https://doi.org/10.1007/s11257-023-09381-y","url":null,"abstract":"<p>In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is difficult, as truly urgent comments are rare. From a data analytics perspective, this represents a <i>highly unbalanced (sparse) dataset</i>. Here, we aim to <i>automate the urgent comments identification process, based on fine-grained learner modelling</i>—to be used for automatic recommendations to instructors. To showcase and compare these models, we apply them to the <i>first gold standard dataset for </i><b><i>U</i></b><i>rgent i</i><b><i>N</i></b><i>structor </i><b><i>I</i></b><i>n</i><b><i>TE</i></b><i>rvention (UNITE)</i>, which we created by labelling FutureLearn MOOC data. We implement both benchmark shallow classifiers and deep learning. Importantly, we not only <i>compare, for the first time for the unbalanced problem, several data balancing techniques</i>, comprising text augmentation, text augmentation with undersampling, and undersampling, but also <i>propose several new pipelines for combining different augmenters for text augmentation</i>. Results show that models with undersampling can predict most urgent cases; and 3X <i>augmentation</i> + <i>undersampling</i> usually attains the best performance. We additionally validate the best models via a generic benchmark dataset (Stanford). As a case study, we showcase how the naïve Bayes with count vector can adaptively support instructors in answering learner questions/comments, potentially saving time or increasing efficiency in supporting learners. Finally, we show that the errors from the classifier mirrors the disagreements between annotators. Thus, our proposed algorithms perform at least as well as a ‘super-diligent’ human instructor (with the time to consider all comments).</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"202 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496554","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}

{"title":"What influences users to provide explicit feedback? A case of food delivery recommenders","authors":"Matthew Haruyama, Kazuyoshi Hidaka","doi":"10.1007/s11257-023-09385-8","DOIUrl":"https://doi.org/10.1007/s11257-023-09385-8","url":null,"abstract":"<p>Although various forms of explicit feedback such as ratings and reviews are important for recommenders, they are notoriously difficult to collect. However, beyond attributing these difficulties to user effort, we know surprisingly little about user motivations. Here, we provide a behavioral account of explicit feedback’s sparsity problem by modeling a range of constructs on the rating and review intentions of US food delivery platform users, using data collected from a structured survey (<i>n</i> = 796). Our model, combining the Technology Acceptance Model and Theory of Planned Behavior, revealed that standard industry practices for feedback collection appear misaligned with key psychological influences of behavioral intentions. Most notably, rating and review intentions were most influenced by subjective norms. This means that while most systems directly request feedback in <i>user-to-provider relationships</i>, eliciting them through social ties that manifest in <i>user-to-user relationships</i> is likely more effective. Secondly, our hypothesized dimensions of feedback’s perceived usefulness recorded insubstantial effect sizes on feedback intentions. These findings offered clues for practitioners to improve the connection between providing behaviors and recommendation benefits through contextualized messaging. In addition, perceived pressure and users’ high stated ability to provide feedback recorded insignificant effects, suggesting that frequent feedback requests may be ineffective. Lastly, privacy concerns recorded insignificant effects, hinting that the personalization-privacy paradox might not apply to preference information such as ratings and reviews. Our results provide a novel understanding of explicit feedback intentions to improve feedback collection in food delivery and beyond.</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"201 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496555","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}

{"title":"Leveraging response times in learning environments: opportunities and challenges","authors":"Radek Pelánek","doi":"10.1007/s11257-023-09386-7","DOIUrl":"https://doi.org/10.1007/s11257-023-09386-7","url":null,"abstract":"Abstract Computer-based learning environments can easily collect student response times. These can be used for multiple purposes, such as modeling student knowledge and affect, domain modeling, and cheating detection. However, to fully leverage them, it is essential to understand the properties of response times and associated caveats. In this study, we delve into the properties of response time distributions, including the influence of aberrant student behavior on response times. We then provide an overview of modeling approaches that use response times and discuss potential applications of response times for guiding the adaptive behavior of learning environments.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"16 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934626","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}

{"title":"“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices","authors":"Alain D. Starke, Cataldo Musto, Amon Rapp, Giovanni Semeraro, Christoph Trattner","doi":"10.1007/s11257-023-09377-8","DOIUrl":"https://doi.org/10.1007/s11257-023-09377-8","url":null,"abstract":"Abstract Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 ( $$N=502$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>502</mml:mn> </mml:mrow> </mml:math> ), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 ( $$N=504$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>504</mml:mn> </mml:mrow> </mml:math> ), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"17 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135265919","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}

{"title":"Hybrid session-aware recommendation with feature-based models","authors":"Josef Bauer, Dietmar Jannach","doi":"10.1007/s11257-023-09379-6","DOIUrl":"https://doi.org/10.1007/s11257-023-09379-6","url":null,"abstract":"Abstract Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based ( “session-aware” ) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR ) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350689","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}

{"title":"Digitally nudging users to explore off-profile recommendations: here be dragons","authors":"Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Daniela Damian, Marcelo Garcia Manzato","doi":"10.1007/s11257-023-09378-7","DOIUrl":"https://doi.org/10.1007/s11257-023-09378-7","url":null,"abstract":"Abstract In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging , i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1064</mml:mn> </mml:mrow> </mml:math> ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591951","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}

{"title":"Harnessing distributional semantics to build context-aware justifications for recommender systems","authors":"Cataldo Musto, Giuseppe Spillo, Giovanni Semeraro","doi":"10.1007/s11257-023-09382-x","DOIUrl":"https://doi.org/10.1007/s11257-023-09382-x","url":null,"abstract":"Abstract This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects . Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135815746","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}