Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization最新文献

Personalized Multilingual Search - Predicting Search Result List Language Preferences 个性化多语言搜索-预测搜索结果列表语言偏好

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394877

B. Steichen, Carla Castillo, Kevin Scroggins

{"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}

引用次数: 4

A Gentle Introduction to Recommendation as Counterfactual Policy Learning 推荐作为反事实策略学习的简单介绍

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3398666

Flavian Vasile, D. Rohde, Olivier Jeunen, Amine Benhalloum

{"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}

引用次数: 8

An Exploratory Study on Techniques for Quantitative Assessment of Stroke Rehabilitation Exercises 脑卒中康复训练定量评价技术的探索性研究

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394872

Min Hun Lee, D. Siewiorek, A. Smailagic, A. Bernardino, S. Badia

{"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}

引用次数: 12

Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models 基于整体用户模型的知识感知食物推荐系统

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394880

C. Musto, C. Trattner, A. Starke, G. Semeraro

引用次数: 26

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels 注意差距:探索时尚零售渠道的购物偏好

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394866

Matthias Wölbitsch, Thomas Hasler, Simon Walk, D. Helic

{"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}

引用次数: 3

Tracking and Modeling Subjective Well-Being Using Smartphone-Based Digital Phenotype 使用基于智能手机的数字表型跟踪和建模主观幸福感

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394855

S. Rhim, Uichin Lee, Kyungsik Han

引用次数: 8

HAAPIE 2020: 5th International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments HAAPIE 2020:第五届自适应和个性化互动环境中的人类方面国际研讨会

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3398672

Panagiotis Germanakos, V. Dimitrova, B. Steichen, A. Piotrkowicz

引用次数: 0

The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations 项目特征和用户特征对用户感知推荐偶然性的影响

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394863

Ningxia Wang, L. Chen, Y. Yang

引用次数: 8

Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations 基于对话的会话推荐预测用户意图和满意度

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3394856

Wanling Cai, L. Chen

{"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}

引用次数: 40

A Personalised Intervention Model for Improving the Effectiveness of Driving-Behaviour Apps 提高驾驶行为应用程序有效性的个性化干预模型

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2020-07-07 DOI: 10.1145/3340631.3398680

Jawwad Baig

{"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}

引用次数: 1