Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, M. Degemmis, G. Semeraro
The use of social media, like Facebook, Twitter and LinkedIn, is nowadays very common and quite for sure each one of us has at least a digital profile on them. The information left of these platforms such as likes, posts, tweets and photos are very informative and can be used for deducting our preferences, tendencies and behaviors. The analysis of the social media footprints has become a relevant research topic in the last decade and many works have demonstrated how to extract some traits of the user's affective sphere. In this paper, we focus on the prediction of empathic tendencies of a subject as an index of the influence of emotions during decisional processes. This value can be included in the user profile and can be relevant in some scenarios, such as music and movie recommender systems, where the emotional component is strongly delineated. We propose an approach of empathy level prediction based on a linear regression algorithm over Facebook profiles. We use a word2vec representation of the textual contents of the user's time-line posts, a LDA and SVD vector representation of the user's likes and other general descriptive data. The evaluation performed has demonstrated the validity of the approach for predicting the empathy tendency and the results have showed some relevant correlations with some specific groups of user's descriptive features.
{"title":"User's Social Media Profile as Predictor of Empathy","authors":"Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, M. Degemmis, G. Semeraro","doi":"10.1145/3099023.3099103","DOIUrl":"https://doi.org/10.1145/3099023.3099103","url":null,"abstract":"The use of social media, like Facebook, Twitter and LinkedIn, is nowadays very common and quite for sure each one of us has at least a digital profile on them. The information left of these platforms such as likes, posts, tweets and photos are very informative and can be used for deducting our preferences, tendencies and behaviors. The analysis of the social media footprints has become a relevant research topic in the last decade and many works have demonstrated how to extract some traits of the user's affective sphere. In this paper, we focus on the prediction of empathic tendencies of a subject as an index of the influence of emotions during decisional processes. This value can be included in the user profile and can be relevant in some scenarios, such as music and movie recommender systems, where the emotional component is strongly delineated. We propose an approach of empathy level prediction based on a linear regression algorithm over Facebook profiles. We use a word2vec representation of the textual contents of the user's time-line posts, a LDA and SVD vector representation of the user's likes and other general descriptive data. The evaluation performed has demonstrated the validity of the approach for predicting the empathy tendency and the results have showed some relevant correlations with some specific groups of user's descriptive features.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. D. Medio, Fabio Gasparetti, C. Limongelli, F. Sciarrone, M. Temperini
During the phases of course construction, in Learning Management Systems, a teacher can be valuably helped by system's recommendations about learning objects to include in the course. A usual protocol is in that the teacher performs a query, looking for suitable learning material, and the system proposes a list of learning objects, with information shown for each one; then the teacher is supposed to make her choice, basing on the displayed information. Here we present a Recommender System for Learning Objects retrieved from Learning Objects Repositories, that is based on a ``social teacher model", based on the similarities with the teacher in the system, and the potential model evolutions over time. The proposed system is available as a Moodle plug-in. In the paper we show the details of the information decorating the learning objects retrieved after a query, the definition of the teacher model, and the similarity measure underlying the recommendation strategy.
{"title":"Course-Driven Teacher Modeling for Learning Objects Recommendation in the Moodle LMS","authors":"C. D. Medio, Fabio Gasparetti, C. Limongelli, F. Sciarrone, M. Temperini","doi":"10.1145/3099023.3099037","DOIUrl":"https://doi.org/10.1145/3099023.3099037","url":null,"abstract":"During the phases of course construction, in Learning Management Systems, a teacher can be valuably helped by system's recommendations about learning objects to include in the course. A usual protocol is in that the teacher performs a query, looking for suitable learning material, and the system proposes a list of learning objects, with information shown for each one; then the teacher is supposed to make her choice, basing on the displayed information. Here we present a Recommender System for Learning Objects retrieved from Learning Objects Repositories, that is based on a ``social teacher model\", based on the similarities with the teacher in the system, and the potential model evolutions over time. The proposed system is available as a Moodle plug-in. In the paper we show the details of the information decorating the learning objects retrieved after a query, the definition of the teacher model, and the similarity measure underlying the recommendation strategy.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116276305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research has shown that Competition is a powerful intrinsic motivator of behavior change. However, little is known about the predictors of its persuasiveness and the moderating effect of culture. In this paper, we investigate the predictors of "the persuasiveness of Competition" (i.e. Competition) using three social influence con-structs: Reward, Social Comparison and Social Learning. Using a sample of 287 participants, comprising 213 individualists and 74 collectivists, we explored the interrelationships among the four social influence constructs and how the two cultures differ and/or are similar. Our global model, which accounts for 46% of the variation in Competition, reveals that Reward has the strongest influence on Competition, followed by Social Comparison. However, the model shows that Social Learning has no significant influence on Competition. Finally, our multigroup analysis reveals that, for our population sample, culture does not moderate the interrelationships among the four constructs. Our findings suggest that designers of gamified applications can employ Reward, Social Comparison and Competition as co-persuasive strategies to motivate behavior change for both cultures, as the susceptibilities of users to Reward and Social Comparison are predictors of their susceptibility to Competition.
{"title":"Investigation of the Social Predictors of Competitive Behavior and the Moderating Effect of Culture","authors":"Kiemute Oyibo, Rita Orji, Julita Vassileva","doi":"10.1145/3099023.3099113","DOIUrl":"https://doi.org/10.1145/3099023.3099113","url":null,"abstract":"Research has shown that Competition is a powerful intrinsic motivator of behavior change. However, little is known about the predictors of its persuasiveness and the moderating effect of culture. In this paper, we investigate the predictors of \"the persuasiveness of Competition\" (i.e. Competition) using three social influence con-structs: Reward, Social Comparison and Social Learning. Using a sample of 287 participants, comprising 213 individualists and 74 collectivists, we explored the interrelationships among the four social influence constructs and how the two cultures differ and/or are similar. Our global model, which accounts for 46% of the variation in Competition, reveals that Reward has the strongest influence on Competition, followed by Social Comparison. However, the model shows that Social Learning has no significant influence on Competition. Finally, our multigroup analysis reveals that, for our population sample, culture does not moderate the interrelationships among the four constructs. Our findings suggest that designers of gamified applications can employ Reward, Social Comparison and Competition as co-persuasive strategies to motivate behavior change for both cultures, as the susceptibilities of users to Reward and Social Comparison are predictors of their susceptibility to Competition.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is our great pleasure to welcome you to the UMAP 2017 Doctoral Consortium (DC). The ACM UMAP 2017 Conference, following a tradition started in 1994, includes a DC aiming to welcome, nurture and guide doctoral students of the field. The DC session provides them with an opportunity to describe and obtain constructive feedback and advice on their research work and plans from a panel of distinguished research faculty.
{"title":"UMAP 2017 Doctoral Consortium Chairs' Welcome","authors":"Panagiotis Germanakos, K. Yacef","doi":"10.1145/3099023.3099024","DOIUrl":"https://doi.org/10.1145/3099023.3099024","url":null,"abstract":"It is our great pleasure to welcome you to the UMAP 2017 Doctoral Consortium (DC). The ACM UMAP 2017 Conference, following a tradition started in 1994, includes a DC aiming to welcome, nurture and guide doctoral students of the field. The DC session provides them with an opportunity to describe and obtain constructive feedback and advice on their research work and plans from a panel of distinguished research faculty.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is our great pleasure to welcome you to the UMAP 2017 EvalUMAP workshop. The purpose of this workshop series is to establish comparative evaluation tasks and a suitable framework to support researchers in the user modelling and personalisation research field in comparing their research to that of others. In 2016 we discussed the key challenges in performing such comparative evaluations. This year we focus on the challenges of identifying appropriate datasets and methods. In particular, the planned outcomes of the workshop this year are as follows: (1) Understand the challenges and requirements related to the design of a shared task approach in User Modeling, Adaptation and Personalization space, (2) identify suitable and publicly accessible datasets that overcome the previous identified challenges and requirements and (3) using the identified datasets start designing shared evaluation tasks that will be performed throughout 2017 and be presented at UMAP 2018.
{"title":"2nd International EvalUMAP Workshop (EvalUMAP2017) Chairs' Preface & Organization","authors":"Owen Conlan, A. Staikopoulos","doi":"10.1145/3099023.3099041","DOIUrl":"https://doi.org/10.1145/3099023.3099041","url":null,"abstract":"It is our great pleasure to welcome you to the UMAP 2017 EvalUMAP workshop. The purpose of this workshop series is to establish comparative evaluation tasks and a suitable framework to support researchers in the user modelling and personalisation research field in comparing their research to that of others. In 2016 we discussed the key challenges in performing such comparative evaluations. This year we focus on the challenges of identifying appropriate datasets and methods. In particular, the planned outcomes of the workshop this year are as follows: (1) Understand the challenges and requirements related to the design of a shared task approach in User Modeling, Adaptation and Personalization space, (2) identify suitable and publicly accessible datasets that overcome the previous identified challenges and requirements and (3) using the identified datasets start designing shared evaluation tasks that will be performed throughout 2017 and be presented at UMAP 2018.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125429257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Personalizing interactive systems including games increases their effectiveness. This paper explores and compares two main approaches to personalization: system-controlled and user-controlled adaptation. The results of large-scale exploratory studies of 1768 users show that both techniques to personalizing systems share seven common strengths of increasing users' perception of a system's relevance, usefulness, interactivity, ease of use, credibility and trust, and also increases users' self-efficacy. The results also reveal some unique strengths and weaknesses peculiar to each of the approaches that designers should take into consideration when deciding on a suitable adaptation technique to use in personalizing their systems. Users prefer system- over user-controlled adaptation.
{"title":"A Comparison of System-Controlled and User-Controlled Personalization Approaches","authors":"Rita Orji, Kiemute Oyibo, G. F. Tondello","doi":"10.1145/3099023.3099116","DOIUrl":"https://doi.org/10.1145/3099023.3099116","url":null,"abstract":"Personalizing interactive systems including games increases their effectiveness. This paper explores and compares two main approaches to personalization: system-controlled and user-controlled adaptation. The results of large-scale exploratory studies of 1768 users show that both techniques to personalizing systems share seven common strengths of increasing users' perception of a system's relevance, usefulness, interactivity, ease of use, credibility and trust, and also increases users' self-efficacy. The results also reveal some unique strengths and weaknesses peculiar to each of the approaches that designers should take into consideration when deciding on a suitable adaptation technique to use in personalizing their systems. Users prefer system- over user-controlled adaptation.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hanna Schäfer, Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, F. Ricci, A. Said
In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.
{"title":"User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity","authors":"Hanna Schäfer, Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, F. Ricci, A. Said","doi":"10.1145/3099023.3099108","DOIUrl":"https://doi.org/10.1145/3099023.3099108","url":null,"abstract":"In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter Knees, Kristina Andersen, A. Said, M. Tkalcic
It is our great pleasure to welcome you to the UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). Following the successful first edition of the workshop at UMAP 2016, we are happy to see a continuing and increasing interest in the workshop's topics. As with the first edition, for the second edition we were able to accept four highly relevant submissions, allowing us to discuss the challenges of recommending unexpected, nonetheless relevant and impactful artifacts during a focused half-day workshop. With the workshop being originally motivated by interviews with music creators and producers who articulated a strong rejection of "more-of-the-same" search engines and recommender systems as they challenge their notion of originality and, ultimately, pose a threat to their artistic identity, we realized that a demand for adaptive and personalized systems that not only have the capability to surprise, but also to oppose and even obstruct can be found in a wider field. In fact, this coincides with ongoing trends to deal with and escape generally negatively connoted effects of automatic recommender systems, such as the so-called "filter-bubble". Apart from the potential dangers of such effects on the unreflecting user, there seems to be a growing impression that collaborative, as well as content-based recommender systems keep making obvious, uninspiring, and therefore disengaging suggestions based on previous interactions. Over the last years, this has emphasized the value of system qualities beyond pure accuracy, e.g., diversity, novelty, serendipity, or unexpectedness, to keep the user satisfied. In fact, these approaches to kicking the user out of his or her "comfort zone" seem to be highly promising methods to increase satisfaction with a system in the long run.
{"title":"UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems: Organizers' Welcome & Organization","authors":"Peter Knees, Kristina Andersen, A. Said, M. Tkalcic","doi":"10.1145/3099023.3099095","DOIUrl":"https://doi.org/10.1145/3099023.3099095","url":null,"abstract":"It is our great pleasure to welcome you to the UMAP 2017 Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). Following the successful first edition of the workshop at UMAP 2016, we are happy to see a continuing and increasing interest in the workshop's topics. As with the first edition, for the second edition we were able to accept four highly relevant submissions, allowing us to discuss the challenges of recommending unexpected, nonetheless relevant and impactful artifacts during a focused half-day workshop. With the workshop being originally motivated by interviews with music creators and producers who articulated a strong rejection of \"more-of-the-same\" search engines and recommender systems as they challenge their notion of originality and, ultimately, pose a threat to their artistic identity, we realized that a demand for adaptive and personalized systems that not only have the capability to surprise, but also to oppose and even obstruct can be found in a wider field. In fact, this coincides with ongoing trends to deal with and escape generally negatively connoted effects of automatic recommender systems, such as the so-called \"filter-bubble\". Apart from the potential dangers of such effects on the unreflecting user, there seems to be a growing impression that collaborative, as well as content-based recommender systems keep making obvious, uninspiring, and therefore disengaging suggestions based on previous interactions. Over the last years, this has emphasized the value of system qualities beyond pure accuracy, e.g., diversity, novelty, serendipity, or unexpectedness, to keep the user satisfied. In fact, these approaches to kicking the user out of his or her \"comfort zone\" seem to be highly promising methods to increase satisfaction with a system in the long run.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127813554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evaluation of personalized systems is a complicated endeavor. First, evaluation goals, methods and criteria are manifold and have to be carefully selected to fit the actual application scenario and the scope of the evaluated system. Second, it is considerably harder to locate the source of problems, compared to non-adaptive systems where problems most often reside on the UI level. Thus, in the past, a layered evaluation approach for personalized systems has been proposed that distinguishes between five layers that can theoretically all be the source of problems (e.g., collection of input data or adaptation decision). This paper outlines a use case related to personalized interaction comprising i) modeling a user's interaction abilities, ii) recommending interaction methods and devices that fit the user's individual needs, and iii) personalized system behavior and reaction to user input. Next, the paper describes experiences with an evaluation process using the layered evaluation framework.
{"title":"Layered Evaluation of a Personalized Interaction Approach","authors":"Mirjam Augstein, Thomas Neumayr","doi":"10.1145/3099023.3099043","DOIUrl":"https://doi.org/10.1145/3099023.3099043","url":null,"abstract":"Evaluation of personalized systems is a complicated endeavor. First, evaluation goals, methods and criteria are manifold and have to be carefully selected to fit the actual application scenario and the scope of the evaluated system. Second, it is considerably harder to locate the source of problems, compared to non-adaptive systems where problems most often reside on the UI level. Thus, in the past, a layered evaluation approach for personalized systems has been proposed that distinguishes between five layers that can theoretically all be the source of problems (e.g., collection of input data or adaptation decision). This paper outlines a use case related to personalized interaction comprising i) modeling a user's interaction abilities, ii) recommending interaction methods and devices that fit the user's individual needs, and iii) personalized system behavior and reaction to user input. Next, the paper describes experiences with an evaluation process using the layered evaluation framework.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Driessens, I. Koprinska, O. Santos, E. Smirnov, K. Yacef, Osmar R Zaiane
Welcome to the 4th International Workshop on Educational Recommender Systems (EdRecSys) held in conjunction with the 25th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP 2017). Recommender systems have become increasingly popular in recent years, helping us to make decisions about what products to buy, what movies to watch, what books to read or who to date. While these systems have shown their effectiveness in e-commerce, music and social networks, the field of education is an emerging and very promising application area. The educational environment is no longer limited to face-to-face classes; it includes online learning and activities using Technology Enhanced Learning (TEL), Learning Management Systems (LMS) and Massive Open Online Courses (MOOC), all of which can benefit from the application of recommender systems to alleviate information overload and improve personalisation, to better meet the needs of the individual student. For example, high school and university students can be provided with recommendations about: (i) suitable degrees and courses, based on their background, preferences and prior experience; (ii) project and thesis topics, and appropriate supervisors; (iii) internships and jobs; (iv) other students to work with (to do group work or peer learning); (v) suitable learning resources based on their knowledge, skills and learning style, e.g. books, tutorials, activities, etc. This workshop has brought together researchers and practitioners from the areas of user modeling and personalisation, recommender systems, education, data mining, learning analytics, intelligent tutoring systems and other related disciplines, to explore the use of recommender systems in education, share their experience and discuss the challenges and open research topics in the design and deployment of effective solutions.
{"title":"UMAP 2017 EdRecSys Workshop Organizers' Welcome & Organization","authors":"K. Driessens, I. Koprinska, O. Santos, E. Smirnov, K. Yacef, Osmar R Zaiane","doi":"10.1145/3099023.3099033","DOIUrl":"https://doi.org/10.1145/3099023.3099033","url":null,"abstract":"Welcome to the 4th International Workshop on Educational Recommender Systems (EdRecSys) held in conjunction with the 25th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP 2017). Recommender systems have become increasingly popular in recent years, helping us to make decisions about what products to buy, what movies to watch, what books to read or who to date. While these systems have shown their effectiveness in e-commerce, music and social networks, the field of education is an emerging and very promising application area. The educational environment is no longer limited to face-to-face classes; it includes online learning and activities using Technology Enhanced Learning (TEL), Learning Management Systems (LMS) and Massive Open Online Courses (MOOC), all of which can benefit from the application of recommender systems to alleviate information overload and improve personalisation, to better meet the needs of the individual student. For example, high school and university students can be provided with recommendations about: (i) suitable degrees and courses, based on their background, preferences and prior experience; (ii) project and thesis topics, and appropriate supervisors; (iii) internships and jobs; (iv) other students to work with (to do group work or peer learning); (v) suitable learning resources based on their knowledge, skills and learning style, e.g. books, tutorials, activities, etc. This workshop has brought together researchers and practitioners from the areas of user modeling and personalisation, recommender systems, education, data mining, learning analytics, intelligent tutoring systems and other related disciplines, to explore the use of recommender systems in education, share their experience and discuss the challenges and open research topics in the design and deployment of effective solutions.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132137351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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