With estimates suggesting that half of the world's population learns or speaks at least two languages, Web information access systems such as Web search engines need to cater for an increasing variety of individual language proficiencies and preferences. However, while significant advances have been made regarding the handling, retrieval, and automatic translation of multilingual information, there has been a relative lack of user-centered research aiming to support individual users' multilingual abilities. To address this research gap, this paper presents a series of user studies and experiments that aim to inform novel search solutions that specifically support multilingual users. In particular, the experiments presented in this paper examine the extent to which a system can predict, for a given query, what language(s) a multilingual user would prefer the search results to be in. Results from our studies show that such predictions can statistically significantly outperform a baseline model, and that users' languages and proficiencies, their current location, as well as the search topic domain and type all influence the prediction results.
{"title":"Personalized Multilingual Search - Predicting Search Result List Language Preferences","authors":"B. Steichen, Carla Castillo, Kevin Scroggins","doi":"10.1145/3340631.3394877","DOIUrl":"https://doi.org/10.1145/3340631.3394877","url":null,"abstract":"With estimates suggesting that half of the world's population learns or speaks at least two languages, Web information access systems such as Web search engines need to cater for an increasing variety of individual language proficiencies and preferences. However, while significant advances have been made regarding the handling, retrieval, and automatic translation of multilingual information, there has been a relative lack of user-centered research aiming to support individual users' multilingual abilities. To address this research gap, this paper presents a series of user studies and experiments that aim to inform novel search solutions that specifically support multilingual users. In particular, the experiments presented in this paper examine the extent to which a system can predict, for a given query, what language(s) a multilingual user would prefer the search results to be in. Results from our studies show that such predictions can statistically significantly outperform a baseline model, and that users' languages and proficiencies, their current location, as well as the search topic domain and type all influence the prediction results.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123415093","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}
Flavian Vasile, D. Rohde, Olivier Jeunen, Amine Benhalloum
The objective of this tutorial is to give a structured overview of the conceptual frameworks behind current state-of-the-art recommender systems, explain their underlying assumptions, the resulting methods and their shortcomings, and to introduce an exciting new class of approaches that frames the task of recommendation as a counterfactual policy learning problem. The tutorial can be divided into two modules. In module 1, participants learn about current approaches for building real-world recommender systems that comprise mainly of two frameworks, namely: recommendation as optimal auto-completion of user behaviour and recommendation as reward modelling. In module 2, we present the framework of recommendation as a counterfactual policy learning problem and go over the theoretical guarantees that address the shortcomings of the previous frameworks. We then proceed to go over the associated algorithms and test them against classical methods in RecoGym, an open-source recommendation simulation environment. Overall, we believe the subject of the course is extremely actual and fills a gap between the consecrated recommendation frameworks and the cutting edge research and sets the stage for future advances in the field.
{"title":"A Gentle Introduction to Recommendation as Counterfactual Policy Learning","authors":"Flavian Vasile, D. Rohde, Olivier Jeunen, Amine Benhalloum","doi":"10.1145/3340631.3398666","DOIUrl":"https://doi.org/10.1145/3340631.3398666","url":null,"abstract":"The objective of this tutorial is to give a structured overview of the conceptual frameworks behind current state-of-the-art recommender systems, explain their underlying assumptions, the resulting methods and their shortcomings, and to introduce an exciting new class of approaches that frames the task of recommendation as a counterfactual policy learning problem. The tutorial can be divided into two modules. In module 1, participants learn about current approaches for building real-world recommender systems that comprise mainly of two frameworks, namely: recommendation as optimal auto-completion of user behaviour and recommendation as reward modelling. In module 2, we present the framework of recommendation as a counterfactual policy learning problem and go over the theoretical guarantees that address the shortcomings of the previous frameworks. We then proceed to go over the associated algorithms and test them against classical methods in RecoGym, an open-source recommendation simulation environment. Overall, we believe the subject of the course is extremely actual and fills a gap between the consecrated recommendation frameworks and the cutting edge research and sets the stage for future advances in the field.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122515213","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}
Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia
Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient's quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to patients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learning (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected therapist's knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.
{"title":"An Exploratory Study on Techniques for Quantitative Assessment of Stroke Rehabilitation Exercises","authors":"Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia","doi":"10.1145/3340631.3394872","DOIUrl":"https://doi.org/10.1145/3340631.3394872","url":null,"abstract":"Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient's quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to patients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learning (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected therapist's knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117241234","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}
Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.
{"title":"Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models","authors":"C. Musto, C. Trattner, A. Starke, G. Semeraro","doi":"10.1145/3340631.3394880","DOIUrl":"https://doi.org/10.1145/3340631.3394880","url":null,"abstract":"Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130370669","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}
Matthias Wölbitsch, Thomas Hasler, Simon Walk, D. Helic
Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.
{"title":"Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels","authors":"Matthias Wölbitsch, Thomas Hasler, Simon Walk, D. Helic","doi":"10.1145/3340631.3394866","DOIUrl":"https://doi.org/10.1145/3340631.3394866","url":null,"abstract":"Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131036192","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}
Subjective well-being (SWB) is a well-studied, widely used construct that refers to how people feel and think about their lives as one of many comprehensive perspectives on well-being. Much research has analyzed the role and utilization of technologies to improve one's SWB; however, especially when it comes to user modeling, multifaceted and variational aspects of SWB are less frequently considered. This paper presents an analysis on identifying factors for smartphone-based data on SWB and modeling SWB changes, based on a four-month user study with 78 college students. Our regression analysis highlights the significance of user attributes (e.g., personality, self-esteem) on SWB and salient factors derived from smartphone data (e.g., time spent on campus, ratio of standing/sitting stationary, expenses) that significantly account for SWB. Our classification analysis shows the potential for detecting SWB changes with reasonable performance, as well as for improving a model to be more tailored to individuals.
{"title":"Tracking and Modeling Subjective Well-Being Using Smartphone-Based Digital Phenotype","authors":"S. Rhim, Uichin Lee, Kyungsik Han","doi":"10.1145/3340631.3394855","DOIUrl":"https://doi.org/10.1145/3340631.3394855","url":null,"abstract":"Subjective well-being (SWB) is a well-studied, widely used construct that refers to how people feel and think about their lives as one of many comprehensive perspectives on well-being. Much research has analyzed the role and utilization of technologies to improve one's SWB; however, especially when it comes to user modeling, multifaceted and variational aspects of SWB are less frequently considered. This paper presents an analysis on identifying factors for smartphone-based data on SWB and modeling SWB changes, based on a four-month user study with 78 college students. Our regression analysis highlights the significance of user attributes (e.g., personality, self-esteem) on SWB and salient factors derived from smartphone data (e.g., time spent on campus, ratio of standing/sitting stationary, expenses) that significantly account for SWB. Our classification analysis shows the potential for detecting SWB changes with reasonable performance, as well as for improving a model to be more tailored to individuals.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124586526","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}
Panagiotis Germanakos, V. Dimitrova, B. Steichen, A. Piotrkowicz
Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. HAAPIE workshop embraces the essence of the "human-machine co-existence" and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the fifth edition of HAAPIE includes 5 long papers.
{"title":"HAAPIE 2020: 5th International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments","authors":"Panagiotis Germanakos, V. Dimitrova, B. Steichen, A. Piotrkowicz","doi":"10.1145/3340631.3398672","DOIUrl":"https://doi.org/10.1145/3340631.3398672","url":null,"abstract":"Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. HAAPIE workshop embraces the essence of the \"human-machine co-existence\" and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the fifth edition of HAAPIE includes 5 long papers.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131208117","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}
Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the "filter bubble" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.
{"title":"The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations","authors":"Ningxia Wang, L. Chen, Y. Yang","doi":"10.1145/3340631.3394863","DOIUrl":"https://doi.org/10.1145/3340631.3394863","url":null,"abstract":"Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the \"filter bubble\" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128143634","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}
To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.
{"title":"Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations","authors":"Wanling Cai, L. Chen","doi":"10.1145/3340631.3394856","DOIUrl":"https://doi.org/10.1145/3340631.3394856","url":null,"abstract":"To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910478","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}
Driving behaviour is key to determining the safety of individuals on the road. It can be argued that understanding driving behaviour and developing methods to improve it will lead to a decrease in accidents and improve citizen safety. At present, most of the work associated with driving behaviour is carried out by insurance companies who use mobile apps and telematic sensors to monitor driving behaviours. These companies are, mainly, capturing driving data to calculate annual premiums rather than to share that data with the drivers. On the academic side, the work focuses on feedback approach and real-time warnings systems. Both commercial and academic research does not consider the significant fact that all drivers are not the same; one-size-fits-all" will not work. This research investigates the scope of personalisation by factors such as age, gender, culture, country and type of driving (e.g. rural or urban) and its impact on driver behaviour. The aim is to improve the effectiveness of driving behaviours systems which can produce meaningful feedback to the driver. Our model suggests that through personalisation, user-modelling and persuasive techniques such as regular feedback reports to drivers (showing their bad driving behaviour), it is possible to improve driving styles and eventually create improved driving behaviour systems. Another positive outcome of this model will be safer roads. We have conducted surveys, used focus groups and interviews to find out the types of driver and their preferences.
{"title":"A Personalised Intervention Model for Improving the Effectiveness of Driving-Behaviour Apps","authors":"Jawwad Baig","doi":"10.1145/3340631.3398680","DOIUrl":"https://doi.org/10.1145/3340631.3398680","url":null,"abstract":"Driving behaviour is key to determining the safety of individuals on the road. It can be argued that understanding driving behaviour and developing methods to improve it will lead to a decrease in accidents and improve citizen safety. At present, most of the work associated with driving behaviour is carried out by insurance companies who use mobile apps and telematic sensors to monitor driving behaviours. These companies are, mainly, capturing driving data to calculate annual premiums rather than to share that data with the drivers. On the academic side, the work focuses on feedback approach and real-time warnings systems. Both commercial and academic research does not consider the significant fact that all drivers are not the same; one-size-fits-all\" will not work. This research investigates the scope of personalisation by factors such as age, gender, culture, country and type of driving (e.g. rural or urban) and its impact on driver behaviour. The aim is to improve the effectiveness of driving behaviours systems which can produce meaningful feedback to the driver. Our model suggests that through personalisation, user-modelling and persuasive techniques such as regular feedback reports to drivers (showing their bad driving behaviour), it is possible to improve driving styles and eventually create improved driving behaviour systems. Another positive outcome of this model will be safer roads. We have conducted surveys, used focus groups and interviews to find out the types of driver and their preferences.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441326","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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