Laura Burbach, Patrick Halbach, M. Ziefle, André Calero Valdez
Today more and more people use social networks and so the differences in personalities of users become more diversified. The same holds true for available news content. To test if regular news and fake news are distributed similarly and to what extent this depends on the personality and behavior of individuals, we conducted a mixed-method study. Through an online questionnaire we measured personality traits of individuals in social networks, how they behave, and how they are connected to each other. Using this data, we developed an agent-based model of an online social network. Using our model, an average of 92% of regular news and 98% of fake news were disseminated to the whole network. Network density turned out to be more important for dissemination than the differences in personality and behavior of individuals. Thus the spread of fake news can not only be addressed by focusing on the personality of individual users and their associated behavior. Systemic approaches---integrating both human and algorithm---must be considered to effectively combat fake news.
{"title":"Who Shares Fake News in Online Social Networks?","authors":"Laura Burbach, Patrick Halbach, M. Ziefle, André Calero Valdez","doi":"10.1145/3320435.3320456","DOIUrl":"https://doi.org/10.1145/3320435.3320456","url":null,"abstract":"Today more and more people use social networks and so the differences in personalities of users become more diversified. The same holds true for available news content. To test if regular news and fake news are distributed similarly and to what extent this depends on the personality and behavior of individuals, we conducted a mixed-method study. Through an online questionnaire we measured personality traits of individuals in social networks, how they behave, and how they are connected to each other. Using this data, we developed an agent-based model of an online social network. Using our model, an average of 92% of regular news and 98% of fake news were disseminated to the whole network. Network density turned out to be more important for dissemination than the differences in personality and behavior of individuals. Thus the spread of fake news can not only be addressed by focusing on the personality of individual users and their associated behavior. Systemic approaches---integrating both human and algorithm---must be considered to effectively combat fake news.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905951","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}
F. Orji, Kiemute Oyibo, Rita Orji, J. Greer, Julita Vassileva
The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.
{"title":"Personalization of Persuasive Technology in Higher Education","authors":"F. Orji, Kiemute Oyibo, Rita Orji, J. Greer, Julita Vassileva","doi":"10.1145/3320435.3320478","DOIUrl":"https://doi.org/10.1145/3320435.3320478","url":null,"abstract":"The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132946","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 research aims to design a mobile persuasive technology (PT) to promote acceptable pro-workplace behaviors and etiquette. As a first step to achieving this, we conducted a user study of 252 subjects from an African organization, to uncover what strategies could be used to model proper behaviors and promote employee's commitment to the ideals, visions and missions of an organization. Leveraging existing workplace behavioral procedures, and socio-cultural strategies, we mapped our findings to their corresponding persuasive techniques. Presently, we employed the iterative design process in developing the mobile PT and the design is informed by our findings. Finally, we will deploy our mobile PT and conduct a large-scale evaluation of public workers in a Nigerian workplace to determine its efficacy to promoting positive workplace etiquette and attitudes. We will employ a mixed-method approach involving both quantitative and qualitative (interview and focus group) for this study.
{"title":"Designing Culturally-appropriate Persuasive Technology to Promote Positive Work Attitudes among Workers in Public Workplaces","authors":"M. Nkwo","doi":"10.1145/3320435.3323465","DOIUrl":"https://doi.org/10.1145/3320435.3323465","url":null,"abstract":"This research aims to design a mobile persuasive technology (PT) to promote acceptable pro-workplace behaviors and etiquette. As a first step to achieving this, we conducted a user study of 252 subjects from an African organization, to uncover what strategies could be used to model proper behaviors and promote employee's commitment to the ideals, visions and missions of an organization. Leveraging existing workplace behavioral procedures, and socio-cultural strategies, we mapped our findings to their corresponding persuasive techniques. Presently, we employed the iterative design process in developing the mobile PT and the design is informed by our findings. Finally, we will deploy our mobile PT and conduct a large-scale evaluation of public workers in a Nigerian workplace to determine its efficacy to promoting positive workplace etiquette and attitudes. We will employ a mixed-method approach involving both quantitative and qualitative (interview and focus group) for this study.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116379612","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}
Interactive adaptive systems powered by Reinforcement Learning (RL) have many potential applications, such as intelligent tutoring systems. In such systems there is typically an external human system designer that is creating, monitoring and modifying the interactive adaptive system, trying to improve its performance on the target outcomes. In this paper we focus on algorithmic foundation of how to help the system designer choose the set of sensors or features to define the observation space used by reinforcement learning agent to make decisions. We present an algorithm, value driven representation (VDR), that can iteratively and adaptively augment the observation space of a reinforcement learning agent so that is sufficient to capture a (near) optimal policy. To do so we introduce a new method to optimistically estimate the value of a policy using offline simulated Monte Carlo rollouts. We evaluate the performance of our approach on standard RL benchmarks with simulated humans and demonstrate significant improvement over prior baselines.
{"title":"Value Driven Representation for Human-in-the-Loop Reinforcement Learning","authors":"Ramtin Keramati, E. Brunskill","doi":"10.1145/3320435.3320471","DOIUrl":"https://doi.org/10.1145/3320435.3320471","url":null,"abstract":"Interactive adaptive systems powered by Reinforcement Learning (RL) have many potential applications, such as intelligent tutoring systems. In such systems there is typically an external human system designer that is creating, monitoring and modifying the interactive adaptive system, trying to improve its performance on the target outcomes. In this paper we focus on algorithmic foundation of how to help the system designer choose the set of sensors or features to define the observation space used by reinforcement learning agent to make decisions. We present an algorithm, value driven representation (VDR), that can iteratively and adaptively augment the observation space of a reinforcement learning agent so that is sufficient to capture a (near) optimal policy. To do so we introduce a new method to optimistically estimate the value of a policy using offline simulated Monte Carlo rollouts. We evaluate the performance of our approach on standard RL benchmarks with simulated humans and demonstrate significant improvement over prior baselines.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122522408","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}
An Unscented Hound for Working Memory (AUHWM) is a new framework for the real-time tracking of human Working Memory (WM) that can be used to adapt computer interfaces to users' available cognitive resources. WM is the part of human cognition responsible for the short term storing and handling of information; it can, in stressful situations, under information overload or when suffering from dementia-like diseases, become severely limited, possibly leading to poor decision making. Our preliminary results suggest that AUHWM can provide a precise and timely assessment of WM capacity, so that the cognitive load a specific task imposes on users can be adapted, e.g., at the User Interface (UI) level. AUHWM is based on a low-level stochastic discrete model of human WM dynamics, implemented as a Gradient-Boosting-derived deterministic algorithm that simulates users' oblivion. AUHWM also performs Unscented Kalman filtering to track users' WM-specific parameters in real time, thus providing a dynamic assessment of their cognitive resources. Our approach has been tested and validated using data collected from Match$ ^2$s, a visual memory game played by 18 users in another study. Going beyond real-time WM tracking, AUHWM is intended to also be used for WM prediction, paving the way to the adaptation of tasks and their UIs in real time as a function of users' cognitive abilities; we detail an example of such an adapted system, and provide experimental evidence this approach could lead to future enhanced WM-adapted UIs.
Unscented Hound for Working Memory (AUHWM)是一种实时跟踪人类工作记忆(WM)的新框架，可用于调整计算机界面以适应用户可用的认知资源。WM是人类认知中负责信息短期存储和处理的部分;在压力大的情况下，在信息超载或患有类似痴呆症的疾病时，它可能会受到严重限制，可能导致决策失误。我们的初步研究结果表明，AUHWM可以提供对认知能力的精确和及时的评估，从而可以适应特定任务对用户施加的认知负荷，例如在用户界面(UI)层面。AUHWM基于人类WM动态的低级随机离散模型，作为梯度增强衍生的确定性算法实现，模拟用户的遗忘。AUHWM还执行Unscented卡尔曼滤波，实时跟踪用户的wm特定参数，从而提供对其认知资源的动态评估。我们的方法已经通过Match$ ^2$s收集的数据进行了测试和验证，Match$ ^2$s是另一项研究中18名用户玩的视觉记忆游戏。除了实时WM跟踪之外，AUHWM还打算用于WM预测，为实时适应任务及其ui作为用户认知能力的功能铺平道路;我们详细介绍了这样一个适应系统的例子，并提供了实验证据，这种方法可以导致未来增强的适应wm的ui。
{"title":"\"An Unscented Hound for Working Memory\" and the Cognitive Adaptation of User Interfaces","authors":"B. Sguerra, P. Jouvelot","doi":"10.1145/3320435.3320443","DOIUrl":"https://doi.org/10.1145/3320435.3320443","url":null,"abstract":"An Unscented Hound for Working Memory (AUHWM) is a new framework for the real-time tracking of human Working Memory (WM) that can be used to adapt computer interfaces to users' available cognitive resources. WM is the part of human cognition responsible for the short term storing and handling of information; it can, in stressful situations, under information overload or when suffering from dementia-like diseases, become severely limited, possibly leading to poor decision making. Our preliminary results suggest that AUHWM can provide a precise and timely assessment of WM capacity, so that the cognitive load a specific task imposes on users can be adapted, e.g., at the User Interface (UI) level. AUHWM is based on a low-level stochastic discrete model of human WM dynamics, implemented as a Gradient-Boosting-derived deterministic algorithm that simulates users' oblivion. AUHWM also performs Unscented Kalman filtering to track users' WM-specific parameters in real time, thus providing a dynamic assessment of their cognitive resources. Our approach has been tested and validated using data collected from Match$ ^2$s, a visual memory game played by 18 users in another study. Going beyond real-time WM tracking, AUHWM is intended to also be used for WM prediction, paving the way to the adaptation of tasks and their UIs in real time as a function of users' cognitive abilities; we detail an example of such an adapted system, and provide experimental evidence this approach could lead to future enhanced WM-adapted UIs.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124639275","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}
User engagement plays a central role in companies and organisations operating online services. A main challenge is to leverage knowledge about the online interaction of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are defining the right metrics and properly optimising for them. A common way that engagement is measured and understood is through the definition and development of metrics of user satisfaction, which can act as proxy of short-term user engagement, mostly at session level. In the context of recommender systems, developing a better understanding of how users interact (implicit signals) with them during their online session is important for developing metrics of user satisfaction. Detecting and understanding implicit signals of user satisfaction are essential for enhancing the quality of the recommendations. When users interact with the recommendations served to them, they leave behind fine-grained traces of interaction patterns, which can be leveraged to predict how satisfying their experience was. This talk will present various works and personal thoughts on how to measure user engagement. It will discuss the definition and development of metrics of user satisfaction that can be used as proxy of user engagement, and will include cases of good, bad and ugly scenarios. An important message will be to show that, when aiming to personalise the recommendations, it is important to consider the heterogeneity of both user and content to formalise the notion of satisfaction, and in turn design the appropriate satisfaction metrics to capture these.
{"title":"Engagement, Metrics and Personalisation: the Good, the Bad and the Ugly","authors":"M. Lalmas","doi":"10.1145/3320435.3323709","DOIUrl":"https://doi.org/10.1145/3320435.3323709","url":null,"abstract":"User engagement plays a central role in companies and organisations operating online services. A main challenge is to leverage knowledge about the online interaction of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are defining the right metrics and properly optimising for them. A common way that engagement is measured and understood is through the definition and development of metrics of user satisfaction, which can act as proxy of short-term user engagement, mostly at session level. In the context of recommender systems, developing a better understanding of how users interact (implicit signals) with them during their online session is important for developing metrics of user satisfaction. Detecting and understanding implicit signals of user satisfaction are essential for enhancing the quality of the recommendations. When users interact with the recommendations served to them, they leave behind fine-grained traces of interaction patterns, which can be leveraged to predict how satisfying their experience was. This talk will present various works and personal thoughts on how to measure user engagement. It will discuss the definition and development of metrics of user satisfaction that can be used as proxy of user engagement, and will include cases of good, bad and ugly scenarios. An important message will be to show that, when aiming to personalise the recommendations, it is important to consider the heterogeneity of both user and content to formalise the notion of satisfaction, and in turn design the appropriate satisfaction metrics to capture these.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307815","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}
Andisheh Partovi, Ingrid Zukerman, Kai Zhan, Nora Hamacher, J. Hohwy
We present insights obtained from a web-based game designed to investigate trust-related factors in a care-taking scenario. The game is set in a retirement village, where elderly residents live in smart homes equipped with monitoring systems. These systems should raise alerts when adverse events happen, but they do not function perfectly (they may issue false alerts or miss true events). Players, who "work'' in the village, perform a primary task, whereby they must ensure the welfare of the residents by attending to adverse events in a timely manner, and a secondary routine task that demands their attention. Our contributions are (1) the game itself, which supports experimentation with various trust-related factors; (2) a methodology for the calibration of the game's parameters; (3) insights from two experiments regarding the relationship between device performance, in particular error type, and trust and user behaviour; and (4) insights from predictive models about factors that influence trust and aspects of user behaviour.
{"title":"Relationship between Device Performance, Trust and User Behaviour in a Care-taking Scenario","authors":"Andisheh Partovi, Ingrid Zukerman, Kai Zhan, Nora Hamacher, J. Hohwy","doi":"10.1145/3320435.3320440","DOIUrl":"https://doi.org/10.1145/3320435.3320440","url":null,"abstract":"We present insights obtained from a web-based game designed to investigate trust-related factors in a care-taking scenario. The game is set in a retirement village, where elderly residents live in smart homes equipped with monitoring systems. These systems should raise alerts when adverse events happen, but they do not function perfectly (they may issue false alerts or miss true events). Players, who \"work'' in the village, perform a primary task, whereby they must ensure the welfare of the residents by attending to adverse events in a timely manner, and a secondary routine task that demands their attention. Our contributions are (1) the game itself, which supports experimentation with various trust-related factors; (2) a methodology for the calibration of the game's parameters; (3) insights from two experiments regarding the relationship between device performance, in particular error type, and trust and user behaviour; and (4) insights from predictive models about factors that influence trust and aspects of user behaviour.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238513","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}
Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the "beyond-accuracy" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graph-theory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.
{"title":"Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems","authors":"Noa Tuval","doi":"10.1145/3320435.3323467","DOIUrl":"https://doi.org/10.1145/3320435.3323467","url":null,"abstract":"Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the \"beyond-accuracy\" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graph-theory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124638834","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}
Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Specifically, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user profiles derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. The experiments show that ECCF outperforms U2UCF and category-based collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists.
{"title":"Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests","authors":"Noemi Mauro, L. Ardissono","doi":"10.1145/3320435.3320458","DOIUrl":"https://doi.org/10.1145/3320435.3320458","url":null,"abstract":"Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Specifically, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user profiles derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. The experiments show that ECCF outperforms U2UCF and category-based collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739729","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}
Thi Ngoc Trang Tran, Müslüm Atas, A. Felfernig, Viet-Man Le, Ralph Samer, Martin Stettinger
Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.
{"title":"Towards Social Choice-based Explanations in Group Recommender Systems","authors":"Thi Ngoc Trang Tran, Müslüm Atas, A. Felfernig, Viet-Man Le, Ralph Samer, Martin Stettinger","doi":"10.1145/3320435.3320437","DOIUrl":"https://doi.org/10.1145/3320435.3320437","url":null,"abstract":"Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114465388","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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