Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization最新文献_第2页

Overcoming Customisation Challenges in Information Dashboards 克服信息仪表板中的定制挑战

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595646

Hatim Alsayahani

引用次数: 0

User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal Characteristics 用户对推荐解释的需求:深入分析项目域和个人特征的作用

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592950

Thi Ngoc Trang Tran, A. Felfernig, Viet-Man Le, Thi Minh Ngoc Chau, Thu Giang Mai

{"title":"User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal Characteristics","authors":"Thi Ngoc Trang Tran, A. Felfernig, Viet-Man Le, Thi Minh Ngoc Chau, Thu Giang Mai","doi":"10.1145/3565472.3592950","DOIUrl":"https://doi.org/10.1145/3565472.3592950","url":null,"abstract":"Explanations can be provided with different goals, such as clarifying how the system works, how well the recommended item meets the user’s preferences, and how an explanation helps the user select an item faster. Although extensive research has been conducted in this research line, not much attention is paid to investigating user needs for explanations. To the best of our knowledge, no studies provide related insights, especially from the perspectives of item domain and personal characteristics. Up to now, it is not completely clear if user needs for explanations change across different item domains and vary according to user characteristics. To analyze these aspects, we developed three web-based prototype recommender systems for low-, average-, and high-involvement item domains and conducted a user study with 553 participants from different countries. Related results show that, in high-involvement item domains, users tend to have a look at explanations when they are not satisfied with the recommended items. An opposite tendency was found in low- and average-involvement item domains. Statistically, there is insufficient evidence to suggest correlations between users’ needs for explanations and item domains or between users’ needs and personal characteristics. However, the descriptive statistics show that users’ need for explanations varies across different item domains. In this study, we also found the best explanation approaches to be used in a specific recommendation domain.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253078","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: 0

Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art Recommendation 一起又分开:个性化视觉艺术推荐的多模态表示学习

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592964

B. Yilma, Luis A Leiva

{"title":"Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art Recommendation","authors":"B. Yilma, Luis A Leiva","doi":"10.1145/3565472.3592964","DOIUrl":"https://doi.org/10.1145/3565472.3592964","url":null,"abstract":"With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal preferences and taste. The task of providing accurate and personalized Visual Art (VA) recommendations is thus a complex one, requiring a deep understanding of the intricate interplay of multiple modalities such as image, textual descriptions, or other metadata. In this paper, we study the nuances of modalities involved in the VA domain (image and text) and how they can be effectively harnessed to provide a truly personalized art experience to users. Particularly, we develop four fusion-based multimodal VA recommendation pipelines and conduct a large-scale user-centric evaluation. Our results indicate that early fusion (i.e, joint multimodal learning of visual and textual features) is preferred over a late fusion of ranked paintings from unimodal models (state-of-the-art baselines) but only if the latent representation space of the multimodal painting embeddings is entangled. Our findings open a new perspective for a better representation learning in the VA RecSys domain.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125017670","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: 0

The Influence of Media Bias on News Recommender Systems 媒体偏见对新闻推荐系统的影响

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595619

Qin Ruan, B. Mac Namee, Ruihai Dong

{"title":"The Influence of Media Bias on News Recommender Systems","authors":"Qin Ruan, B. Mac Namee, Ruihai Dong","doi":"10.1145/3565472.3595619","DOIUrl":"https://doi.org/10.1145/3565472.3595619","url":null,"abstract":"Currently I am at the beginning of my fourth year of a structured PhD programme with an expectation to graduate in May 2024. The advancement of Internet technology has led to the proliferation of accessible online news media, which has overwhelmed people’s lives. Online news platforms have developed personalised recommendation systems to help readers avoid information overload and enhance their experience. However, the filter bubble, one of the side effects of personalised news recommendations, has received severe criticism for limiting readers’ perspectives. Media bias, which is one of the factors causing the “filter bubble” phenomenon, is widely present in news media. It has been extensively studied in the field of social sciences due to its unconscious distortion of readers’ views. Although many studies have focused on examining the effect of media bias on users and their political choices, there is still a lack of direct research on the impact of media bias on news dissemination platforms, such as personalised news recommender systems. My PhD research project aims to explore the influence of media bias on news recommender systems, and understand the factors that accelerate the recommendation of biased news to readers. To help algorithm designers gain insight into the sensitivity of proposed recommendation algorithms to media bias, and to design debiasing algorithms to weaken the impact of media bias on news recommender systems.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133578732","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: 0

Sustainability-oriented Recommender Systems 可持续发展导向的推荐系统

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595617

P. Merinov

{"title":"Sustainability-oriented Recommender Systems","authors":"P. Merinov","doi":"10.1145/3565472.3595617","DOIUrl":"https://doi.org/10.1145/3565472.3595617","url":null,"abstract":"1 CONTEXT AND MOTIVATION Recommender Systems (RSs) are software tools that provide users with personalised access to products and services, which are collectively referred to as items [15]. To accomplish this, RSs employ various machine learning techniques to evaluate the relevance score of each item-user pair and then recommend the most relevant items to the corresponding user. In tourism applications, which is my research focus, RSs suggest to users relevant points of interest (POIs), based on the user’s profile, context, and preferences [14]. Despite its benefits, this user-focused recommendation approach in tourism domain (where RS acts only on behalf of the users) may have long-term negative impacts on the environment and local communities. This is because this approach encourages the development of user-driven tourism, which is known to have detrimental effects on natural and non-natural landscapes and wildlife habitats where tourism takes place [11]. Furthermore, such an approach may even affect tourists’ and local residents’ experiences; for example, with increased noise levels, traffic congestion and reduced social distances due to overcrowding in tourist areas. Sustainable tourism, like guidelines and management practices, can provide a solution to these issues. Deliberately, it aims to reduce the tensions created by the complex interactions between tourists and the environment in order to mitigate the negative environmental impacts of uncontrolled development in pursuit of economic benefits [3]. Although this concept is well known in the tourism literature, it is relatively new in the RS community. My PhD research involves the integration of sustainability-driven goals into RS design to improve tourism development by providing sustainable recommendations","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"24 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133690397","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

Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives 使用图神经网络和相关的超精度视角的用户分析教程

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595616

Erasmo Purificato, Ludovico Boratto, E. D. De Luca

{"title":"Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives","authors":"Erasmo Purificato, Ludovico Boratto, E. D. De Luca","doi":"10.1145/3565472.3595616","DOIUrl":"https://doi.org/10.1145/3565472.3595616","url":null,"abstract":"The proposed tutorial aims to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin by discussing the conceptual foundations of user profiling and GNNs and providing a literature review of the two topics. We will then present a systematic overview of the state-of-the-art GNN architectures designed for user profiling, including the types of data that are typically used for this purpose. We will also discuss ethical considerations and beyond-accuracy perspectives (i.e. fairness and explainability), which can arise within the potential applications of adopting GNNs for user profiling. In the practical session of the tutorial, attendees will have the opportunity to understand concretely how recent GNN models for user profiling are built and trained with open-source tools and publicly available datasets. The audience will also be engaged in investigating the impact of the presented models on case studies involving bias detection and mitigation, as well as user profiles explanations. The tutorial will end with an analysis of existing and emerging open challenges in the field and their future research directions.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114919073","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

Fairness and Sustainability in Multistakeholder Tourism Recommender Systems 多利益相关方旅游推荐系统中的公平性和可持续性

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595607

Ashmi Banerjee

{"title":"Fairness and Sustainability in Multistakeholder Tourism Recommender Systems","authors":"Ashmi Banerjee","doi":"10.1145/3565472.3595607","DOIUrl":"https://doi.org/10.1145/3565472.3595607","url":null,"abstract":"In the travel industry, Tourism Recommender Systems (TRS) are gaining popularity as they simplify trip planning for travelers by offering personalized recommendations for accommodations, activities, destinations, and more. Ensuring fairness in TRS involves considering the needs and viewpoints of different stakeholders, including consumers, item providers, the platform, and society. Although previous research has focused on fairness in TRS from a multistakeholder perspective, little attention has been given to generating sustainable recommendations. This doctoral thesis introduces the concept of Societal Fairness (S-Fairness) to consider the impact of tourism on non-participating stakeholders (society) such as residents, who may be affected by tourism issues such as increased housing prices, environmental pollution, and traffic congestion. The objective of this research is to contribute to the field of TRS by (1) modeling sustainability for societal fairness, (2) developing a fair multistakeholder TRS that balances sustainability concerns with other stakeholders while minimizing trade-offs, and (3) evaluating the approach through user studies and offline dataset evaluation to ensure user acceptance of recommendations.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"49 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911817","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: 2

Combining Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations 结合强化学习和空间接近探索的新用户和新POI推荐

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592966

Davide Massimo, Francesco Ricci

引用次数: 3

Towards a Personalized Online Fake News Taxonomy 走向个性化的在线假新闻分类

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592963

F. Altoe, H. Sofia Pinto

{"title":"Towards a Personalized Online Fake News Taxonomy","authors":"F. Altoe, H. Sofia Pinto","doi":"10.1145/3565472.3592963","DOIUrl":"https://doi.org/10.1145/3565472.3592963","url":null,"abstract":"Fake news has become a serious and destabilizing problem in our increasingly polarized society. The core tasks of detecting and characterizing untrue or misleading online content are quite challenging. News consumers intervention has been identified as a crucial addition to Fake News Detection Systems (FNDS). However, even though detection explanation is starting to gain research momentum, not as much attention has been given to personalization of explanations. As humans are the obvious targets of fake news, explanations that can evoke emotional responses and/or are aligned with individual personality traits and cognitive styles can be leveraged to nudging of the news consumer into a reflective state about the subject, which has been shown to be more effective than the crude presentation of facts in changing pre-conceived beliefs. This paper adds to the main goal of misinformation detection systems, aiming to expand them onto personalized FNDS. It proposes a metric to be used in their evaluation. It offers a definition to help research on the implementation of personalized fake news explanations. And finally, it proposes a personalized fake news taxonomy, discussing its components centered around emotion-based and personality-based explanations. This taxonomy highlights several opportunities for those researching in the area of personalized fake news explanation systems.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425525","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: 0

Combining Heterogeneous Embeddings for Knowledge-Aware Recommendation Models 基于异构嵌入的知识感知推荐模型

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595615

Giuseppe Spillo

引用次数: 0