A. Katifori, M. Vayanou, Angeliki Antoniou, I. Ioannidis, Y. Ioannidis
In this paper, we present the results of a user study focusing on whether the visitors' cultural preferences and expectations relate to different personality traits as defined by the Big Five personality model. We describe the user study procedure and report the correlations discovered between some of the Big Five factors and the participant assessments, over particular aspects of a shared digital storytelling experience. We suggest that the results may notably inform not only the design but also the evaluation of cultural experiences, laying the foundations for a promising line of work.
{"title":"Big Five and Cultural Experiences: Impact from Design to Evaluation","authors":"A. Katifori, M. Vayanou, Angeliki Antoniou, I. Ioannidis, Y. Ioannidis","doi":"10.1145/3314183.3323861","DOIUrl":"https://doi.org/10.1145/3314183.3323861","url":null,"abstract":"In this paper, we present the results of a user study focusing on whether the visitors' cultural preferences and expectations relate to different personality traits as defined by the Big Five personality model. We describe the user study procedure and report the correlations discovered between some of the Big Five factors and the participant assessments, over particular aspects of a shared digital storytelling experience. We suggest that the results may notably inform not only the design but also the evaluation of cultural experiences, laying the foundations for a promising line of work.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"4 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132745864","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}
In this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked open data (LOD), by following semantic links in the LOD graph to identify movies, books, and music artists/songs related to that specific POI. This content is subsequently reranked based on the activity of the user and her friends on social media (i.e., Facebook), in order to provide personalized suggestions.
{"title":"Cross-Domain Recommendation for Enhancing Cultural Heritage Experience","authors":"G. Sansonetti, Fabio Gasparetti, A. Micarelli","doi":"10.1145/3314183.3323869","DOIUrl":"https://doi.org/10.1145/3314183.3323869","url":null,"abstract":"In this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked open data (LOD), by following semantic links in the LOD graph to identify movies, books, and music artists/songs related to that specific POI. This content is subsequently reranked based on the activity of the user and her friends on social media (i.e., Facebook), in order to provide personalized suggestions.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133178550","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}
Understanding a consumer's motivation to shop online with a vendor can help an e-business better understand the attitude of customers and what they look out for in their shopping decision-making process. Equally important in the shopping decision making process is the influence of the perceived quality of products and their price. Understanding how consumers are influenced by the perceived quality and price of products can help e-businesses to improve their customers' shopping experience. To contribute to ongoing research in this area, we investigate the influence of perceived product quality and price on the motivation of e-shoppers to shop online. In particular, we investigate which of perceived quality and price have a greater influence on the consumer's motivation to shop online. We also investigate the moderating effect of income and gender. Using a sample size of 241 e-commerce shoppers, we develop and test a global research model using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Our results suggest that balanced buyers (shoppers who are moderately motivated by convenience and variety seeking but do not plan ahead and are impulse buyers) are more influenced by the relative price of products compared to their quality. In addition, balanced buyers who earn over $30,000 are influenced by the quality of the product compared to those who earn less than $30,000. Furthermore, male shoppers who are motivated by the convenience of online shopping (convenience shoppers) are also influenced by the perceived quality of products compared to female shoppers who are not.
{"title":"Shopping Motivation and the Influence of Perceived Product Quality and Relative Price in E-commerce","authors":"I. Adaji, Kiemute Oyibo, Julita Vassileva","doi":"10.1145/3314183.3323852","DOIUrl":"https://doi.org/10.1145/3314183.3323852","url":null,"abstract":"Understanding a consumer's motivation to shop online with a vendor can help an e-business better understand the attitude of customers and what they look out for in their shopping decision-making process. Equally important in the shopping decision making process is the influence of the perceived quality of products and their price. Understanding how consumers are influenced by the perceived quality and price of products can help e-businesses to improve their customers' shopping experience. To contribute to ongoing research in this area, we investigate the influence of perceived product quality and price on the motivation of e-shoppers to shop online. In particular, we investigate which of perceived quality and price have a greater influence on the consumer's motivation to shop online. We also investigate the moderating effect of income and gender. Using a sample size of 241 e-commerce shoppers, we develop and test a global research model using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Our results suggest that balanced buyers (shoppers who are moderately motivated by convenience and variety seeking but do not plan ahead and are impulse buyers) are more influenced by the relative price of products compared to their quality. In addition, balanced buyers who earn over $30,000 are influenced by the quality of the product compared to those who earn less than $30,000. Furthermore, male shoppers who are motivated by the convenience of online shopping (convenience shoppers) are also influenced by the perceived quality of products compared to female shoppers who are not.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116533771","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}
Distributed Ledger (blockchain) technology provides an alternative for distributed databases. It creates a secure and immutable record of data transactions, thus tracking how data are shared and accessed. The access and operations on data can be regulated via "smart contracts" that allows setting conditions for accessing the data - by whom, for what purpose, for how long, under what conditions, whether access is granted to the original data or just a query /derivative data. In addition, users can benefit from sharing their data by using "smart contracts" that regulate sharing for monetary reward, or for another form of recognition. Both user profile data and user-owned data can be shared in this way, empowering users to benefit from their data, under their own conditions, rather than surrendering it to centralized services. The tutorial will present the basics of distributed ledger technology and smart contracts and will train the participants in using a privacy-preserving user data-sharing framework.
{"title":"User Ownership and Control of Data with Distributed Ledger","authors":"Julita Vassileva, R. Deters","doi":"10.1145/3314183.3340266","DOIUrl":"https://doi.org/10.1145/3314183.3340266","url":null,"abstract":"Distributed Ledger (blockchain) technology provides an alternative for distributed databases. It creates a secure and immutable record of data transactions, thus tracking how data are shared and accessed. The access and operations on data can be regulated via \"smart contracts\" that allows setting conditions for accessing the data - by whom, for what purpose, for how long, under what conditions, whether access is granted to the original data or just a query /derivative data. In addition, users can benefit from sharing their data by using \"smart contracts\" that regulate sharing for monetary reward, or for another form of recognition. Both user profile data and user-owned data can be shared in this way, empowering users to benefit from their data, under their own conditions, rather than surrendering it to centralized services. The tutorial will present the basics of distributed ledger technology and smart contracts and will train the participants in using a privacy-preserving user data-sharing framework.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882175","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}
Georgios Theocharous, Jennifer Healey, S. Mahadevan, Michele A. Saad
Human cognitive biases are numerous and well established. Due to inherent limitations in our knowledge of the world, and computational constraints, our judgments and decisions do not rigidly adhere to the principle of maximizing expected utility. We frequently employ cognitive shortcuts, ignoring relevant information, and make errors in how we store and retrieve items from memory. Human decisions are additionally influenced by moral, emotional and cultural parameters. People often perceive value in a way that is very different from well-established decision-theoretic frameworks, but much of the work on personalization does not capture human cognitive biases. Our central hypothesis is that a new generation of recommendation systems can be designed by explicitly modeling human cognitive biases such as contrast, decoy, distinction, and framing. We are just now beginning to see explicit non-linear models of human risk perception being incorporated into machine learning algorithms, and we believe this trend will accelerate in the near future. In this paper we review today's recommendation systems, give an analysis of their limitations and make an argument for why future recommendation systems should incorporate explicit models of human cognitive bias.
{"title":"Personalizing with Human Cognitive Biases","authors":"Georgios Theocharous, Jennifer Healey, S. Mahadevan, Michele A. Saad","doi":"10.1145/3314183.3323453","DOIUrl":"https://doi.org/10.1145/3314183.3323453","url":null,"abstract":"Human cognitive biases are numerous and well established. Due to inherent limitations in our knowledge of the world, and computational constraints, our judgments and decisions do not rigidly adhere to the principle of maximizing expected utility. We frequently employ cognitive shortcuts, ignoring relevant information, and make errors in how we store and retrieve items from memory. Human decisions are additionally influenced by moral, emotional and cultural parameters. People often perceive value in a way that is very different from well-established decision-theoretic frameworks, but much of the work on personalization does not capture human cognitive biases. Our central hypothesis is that a new generation of recommendation systems can be designed by explicitly modeling human cognitive biases such as contrast, decoy, distinction, and framing. We are just now beginning to see explicit non-linear models of human risk perception being incorporated into machine learning algorithms, and we believe this trend will accelerate in the near future. In this paper we review today's recommendation systems, give an analysis of their limitations and make an argument for why future recommendation systems should incorporate explicit models of human cognitive bias.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125218022","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}
This paper investigates how mobile persuasive system targeting African audience could be designed and tailored to promote employee's commitment to the ideals, visions, and mission of an organization. We conduct a qualitative study with two categories of workers to uncover core factors that influence employee's attitudes to their jobs and map our findings to their matching social influence persuasive techniques. We propose that a persuasive system (PS) employing the social influence strategies could motivate workers towards acceptable positive pro-workplace behaviors and etiquette. The PS allows workers too compare their behaviors against set goals and acceptable standards, compete and compare performances with peers, view and respond to peers' activities, and receive recognition for accomplishing a target task. The system ensures the security of worker's data via the authentication of login credentials while showing them a personalized persuasive display of essential workplace information. We present a prototype persuasive system called "PAULApp" for motivating pro-workplace behaviors and plans for evaluation. PAULApp was designed using the iterative design process and was informed by the findings from the user studies.
{"title":"Personalized Persuasion to Promote Positive Work Attitudes in Public Workplaces","authors":"M. Nkwo, Rita Orji","doi":"10.1145/3314183.3323858","DOIUrl":"https://doi.org/10.1145/3314183.3323858","url":null,"abstract":"This paper investigates how mobile persuasive system targeting African audience could be designed and tailored to promote employee's commitment to the ideals, visions, and mission of an organization. We conduct a qualitative study with two categories of workers to uncover core factors that influence employee's attitudes to their jobs and map our findings to their matching social influence persuasive techniques. We propose that a persuasive system (PS) employing the social influence strategies could motivate workers towards acceptable positive pro-workplace behaviors and etiquette. The PS allows workers too compare their behaviors against set goals and acceptable standards, compete and compare performances with peers, view and respond to peers' activities, and receive recognition for accomplishing a target task. The system ensures the security of worker's data via the authentication of login credentials while showing them a personalized persuasive display of essential workplace information. We present a prototype persuasive system called \"PAULApp\" for motivating pro-workplace behaviors and plans for evaluation. PAULApp was designed using the iterative design process and was informed by the findings from the user studies.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130193904","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}
In this work, we investigate whether privacy and fairness can be simultaneously achieved by a single classifier in several different models. Some of the earliest work on fairness in algorithm design defined fairness as a guarantee of similar outputs for "similar'' input data, a notion with tight technical connections to differential privacy. We study whether tensions exist between differential privacy and statistical notions of fairness, namely Equality of False Positives and Equality of False Negatives (EFP/EFN). We show that even under full distributional access, there are cases where the constraint of differential privacy precludes exact EFP/EFN. We then turn to ask whether one can learn a differentially private classifier which approximately satisfies EFP/EFN, and show the existence of a PAC learner which is private and approximately fair with high probability. We conclude by giving an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.
{"title":"On the Compatibility of Privacy and Fairness","authors":"Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern","doi":"10.1145/3314183.3323847","DOIUrl":"https://doi.org/10.1145/3314183.3323847","url":null,"abstract":"In this work, we investigate whether privacy and fairness can be simultaneously achieved by a single classifier in several different models. Some of the earliest work on fairness in algorithm design defined fairness as a guarantee of similar outputs for \"similar'' input data, a notion with tight technical connections to differential privacy. We study whether tensions exist between differential privacy and statistical notions of fairness, namely Equality of False Positives and Equality of False Negatives (EFP/EFN). We show that even under full distributional access, there are cases where the constraint of differential privacy precludes exact EFP/EFN. We then turn to ask whether one can learn a differentially private classifier which approximately satisfies EFP/EFN, and show the existence of a PAC learner which is private and approximately fair with high probability. We conclude by giving an efficient algorithm for classification that maintains utility and satisfies both privacy and approximate fairness with high probability.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440674","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}
The Conlan whitepaper makes a compelling case for a community-scale means of evaluating algorithms for user modeling, adaptation and personalisation (UMAP). The authors propose an evaluation paradigm focused on a personalisation use case within an open modeling environment. Their use case is one where a mobile interface learns a user's preferences for different notifications in different contexts. Additional use cases could be incorporated in this paradigm to provide contrasting kinds of challenge for personalisation evaluation. In this position paper we summarise a use case where personalisation is achieved by categorising a user and switching their interface to a variant. The user interface is a mobile news app within a platform that also comprises a user modeling function and an interface personalisation service. Comparison of the use cases helps to map the space for the evaluation paradigm.
{"title":"Extending the Evaluation Paradigm for Personalisation: A Categorisation Use Case","authors":"J. Dowell, Marios Constantinides","doi":"10.1145/3314183.3323682","DOIUrl":"https://doi.org/10.1145/3314183.3323682","url":null,"abstract":"The Conlan whitepaper makes a compelling case for a community-scale means of evaluating algorithms for user modeling, adaptation and personalisation (UMAP). The authors propose an evaluation paradigm focused on a personalisation use case within an open modeling environment. Their use case is one where a mobile interface learns a user's preferences for different notifications in different contexts. Additional use cases could be incorporated in this paradigm to provide contrasting kinds of challenge for personalisation evaluation. In this position paper we summarise a use case where personalisation is achieved by categorising a user and switching their interface to a variant. The user interface is a mobile news app within a platform that also comprises a user modeling function and an interface personalisation service. Comparison of the use cases helps to map the space for the evaluation paradigm.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123157114","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}
Due to the rise of available online music, a lot of music consumption is moving from traditional offline media to online sources. Online music sources offer almost an unlimited music collection to its users. Hence, how music is consumed by users (e.g., experts) may differ from traditional offline sources. In this work we explored how musically sophisticated users (i.e. experts) consume online music in terms of diversity. To analyze this, we gathered data from two different sources: Last.fm and Spotify. As expertise is defined by the ubiquitousness of experiences, we calculated different diversity measurements to explore how ubiquitous (in terms of diversity) the listening behaviors of users are. We found that different musical sophistication levels correspond to applying diversity related to specific kind of musical characteristics (i.e., artist or genre). Our results can provide knowledge on how systems should be designed to provide better support to expert users.
{"title":"Exploring Online Music Listening Behaviors of Musically Sophisticated Users","authors":"B. Ferwerda, M. Tkalcic","doi":"10.1145/3314183.3324974","DOIUrl":"https://doi.org/10.1145/3314183.3324974","url":null,"abstract":"Due to the rise of available online music, a lot of music consumption is moving from traditional offline media to online sources. Online music sources offer almost an unlimited music collection to its users. Hence, how music is consumed by users (e.g., experts) may differ from traditional offline sources. In this work we explored how musically sophisticated users (i.e. experts) consume online music in terms of diversity. To analyze this, we gathered data from two different sources: Last.fm and Spotify. As expertise is defined by the ubiquitousness of experiences, we calculated different diversity measurements to explore how ubiquitous (in terms of diversity) the listening behaviors of users are. We found that different musical sophistication levels correspond to applying diversity related to specific kind of musical characteristics (i.e., artist or genre). Our results can provide knowledge on how systems should be designed to provide better support to expert users.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289346","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}
This paper introduces 'RehaBot', a framework for building adaptive serious games in the context of telerehabilitation. RehaBot takes advantage of 3D motion tracking and virtual reality devices, to develop an immersive and gamified telerehabilitation environment. A personalized and adaptive gaming system is developed, which allows patients to perform exercises with the help of embedded virtual assistants, hereafter called 'rehab bots', that are dynamically displayed within scenes to guide the patient through the different sets of gestures required to complete the session. These rehab bots have the ability to learn and adapt to the best level of difficulty in real-time based on the user performance. An intelligent alerting and automatic correction technique is incorporated within our engine, so that pre-calculated gesture patterns are correlated and matched with patients' gestures. Consequently, the system estimates the perceived difficulty of gestures by the patient, and automatically adjusts the game behavior to ensure a highly engaging and adaptive gaming experience. Furthermore, multimodal instructions are conveyed to users with details on joints that are not performing as expected, and to guide them towards improving the current gesture. A pilot study has been conducted to prove the usability and effectiveness of our adaptive physiotherapy solution.
{"title":"RehaBot: Gamified Virtual Assistants Towards Adaptive TeleRehabilitation","authors":"Imad Afyouni, Anas Einea, Abdullah Murad","doi":"10.1145/3314183.3324988","DOIUrl":"https://doi.org/10.1145/3314183.3324988","url":null,"abstract":"This paper introduces 'RehaBot', a framework for building adaptive serious games in the context of telerehabilitation. RehaBot takes advantage of 3D motion tracking and virtual reality devices, to develop an immersive and gamified telerehabilitation environment. A personalized and adaptive gaming system is developed, which allows patients to perform exercises with the help of embedded virtual assistants, hereafter called 'rehab bots', that are dynamically displayed within scenes to guide the patient through the different sets of gestures required to complete the session. These rehab bots have the ability to learn and adapt to the best level of difficulty in real-time based on the user performance. An intelligent alerting and automatic correction technique is incorporated within our engine, so that pre-calculated gesture patterns are correlated and matched with patients' gestures. Consequently, the system estimates the perceived difficulty of gestures by the patient, and automatically adjusts the game behavior to ensure a highly engaging and adaptive gaming experience. Furthermore, multimodal instructions are conveyed to users with details on joints that are not performing as expected, and to guide them towards improving the current gesture. A pilot study has been conducted to prove the usability and effectiveness of our adaptive physiotherapy solution.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"10893 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116862057","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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