Recommendation and Personalization

会議の名前
CHI 2025
PAIGE: Examining Learning Outcomes and Experiences with Personalized AI-Generated Educational Podcasts
要旨

Generative AI is revolutionizing content creation and has the potential to enable real-time, personalized educational experiences. We investigated the effectiveness of converting textbook chapters into AI-generated podcasts and explored the impact of personalizing these podcasts for individual learner profiles. We conducted a 3x3 user study with 180 college students in the United States, comparing traditional textbook reading with both generalized and personalized AI-generated podcasts across three textbook subjects. The personalized podcasts were tailored to students’ majors, interests, and self-described instructional preferences. Our findings show that students found the AI-generated podcast format to be more enjoyable than textbooks and that personalized podcasts led to significantly improved learning outcomes, although this was subject-specific. These results highlight that AI-generated podcasts can offer an engaging and effective modality transformation of textbook material, with personalization enhancing content relevance. We conclude with design recommendations for leveraging AI in education, informed by student feedback.

受賞
Best Paper
著者
Tiffany D.. Do
Drexel University, Philadelphia, Pennsylvania, United States
Usama Bin Shafqat
Google, New York, New York, Pakistan
Elsie Ling
Google, Mountain View, California, United States
Nikhil Sarda
Google, Mountain View, California, United States
DOI

10.1145/3706598.3713460

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713460

動画
Toward Personalizable AI Node Graph Creative Writing Support: Insights on Preferences for Generative AI Features and Information Presentation Across Story Writing Processes
要旨

As story writing requires diverse resources, a single system combining these resources could improve personalization. We leverage the broad capabilities of generative AI to support both more general story writing needs and an understudied but essential aspect: reflection on the moral (lesson) conveyed. Through a formative study (N=12), a user study (N=14), and external evaluation (N=19), we designed, implemented, then studied a prototype plugin for FigJam supporting visualization of the story structure through customizable node graph editing, LLM audience impersonation (chatbot and non-chatbot interfaces), and image and audio generative AI features. Our findings support writers' preference for leveraging unique interplays of our breadth of features to satisfy shifting needs across writing processes, from conveying a moral across audience groups to story writing in general. We discuss how our tool design and findings can inform model bias, personalized writing support, and visualization research.

著者
Hua Xuan Qin
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Guangzhi ZHU
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Mingming Fan
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Pan Hui
The Hong Kong University of Science and Technology, Hong Kong, China
DOI

10.1145/3706598.3713569

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713569

動画
ACKnowledge: A Computational Framework for Human Compatible Affordance-based Interaction Planning in Real-world Contexts
要旨

Intelligent agents coexisting with humans often need to interact with human-shared objects in environments. Thus, agents should plan their interactions based on objects' affordances and the current situation to achieve acceptable outcomes. How to support intelligent agents' planning of affordance-based interactions compatible with human perception and values in real-world contexts remains under-explored. We conducted a formative study identifying the physical, intrapersonal, and interpersonal contexts that count to household human-agent interaction. We then proposed ACKnowledge, a computational framework integrating a dynamic knowledge graph, a large language model, and a vision language model for affordance-based interaction planning in dynamic human environments. In evaluations, ACKnowledge generated acceptable planning results with an understandable process. In real-world simulation tasks, ACKnowledge achieved a high execution success rate and overall acceptability, significantly enhancing usage-rights respectfulness and social appropriateness over baselines. The case study's feedback demonstrated ACKnowledge's negotiation and personalization capabilities toward an understandable planning process.

著者
Ziqi Pan
The Hong Kong University of Science and Technology, Hong Kong, China
Xiucheng Zhang
Sun Yat-sen University, Zhuhai, China
Zisu Li
The Hong Kong University of Science and Technology, Hong Kong SAR, Hong Kong, China
Zhenhui Peng
Sun Yat-sen University, Zhuhai, Guangdong Province, China
Mingming Fan
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Xiaojuan Ma
Hong Kong University of Science and Technology, Hong Kong, Hong Kong
DOI

10.1145/3706598.3713791

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713791

動画
Briteller: Shining a Light on AI Recommendations for Children
要旨

Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system's core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.

著者
Xiaofei Zhou
University of Rochester, Rochester, New York, United States
Yi Zhang
University of California, Irvine, Irvine, California, United States
Yufei Jiang
University of Rochester, Rochester, New York, United States
Yunfan Gong
University of Rochester, Rochester, New York, United States
Chi Zhang
University of Rochester, Rochester, New York, United States
Alissa N.. Antle
Simon Fraser University, Vancouver, British Columbia, Canada
Zhen Bai
University of Rochester, Rochester, New York, United States
DOI

10.1145/3706598.3714106

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714106

動画
Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content
要旨

As personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.

著者
Wenqi Li
Peking University, Beijing, China
Jui-Ching Kuo
National Tsing Hua University, Hsinchu, Taiwan
Manyu Sheng
University of Chinese Academy of Sciences, Beijing, China
Pengyi Zhang
Peking University, Beijing, China
Qunfang Wu
Harvard University, Cambridge, Massachusetts, United States
DOI

10.1145/3706598.3713241

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713241

動画
User Experience of LLM-based Recommendation Systems: A Case of Music Recommendation
要旨

The advancement of large language models (LLMs) now allows users to actively interact with conversational recommendation systems (CRS) and build their own personalized recommendation services tailored to their unique needs and goals. This experience offers users a significantly higher level of controllability compared to traditional RS, enabling an entirely new dimension of recommendation experiences. Building on this context, this study explored the unique experiences that LLM-powered CRS can provide compared to traditional RS. Through a three-week diary study with 12 participants using custom GPTs for music recommendations, we found that LLM-powered CRS can (1) help users clarify implicit needs, (2) support unique exploration, and (3) facilitate a deeper understanding of musical preferences. Based on these findings, we discuss the new design space enabled by LLM-powered CRS and highlight its potential to support more personalized, user-driven recommendation experiences.

著者
Sojeong Yun
KAIST, Daejeon, Korea, Republic of
Youn-kyung Lim
KAIST, Daejeon, Korea, Republic of
DOI

10.1145/3706598.3713347

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713347

動画
Hashtag Re-Appropriation for Audience Control on Recommendation-Driven Social Media Xiaohongshu (rednote)
要旨

Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explores how women on Xiaohongshu (rednote), a recommendation-driven social platform, proactively re-appropriate hashtags (e.g., #宝宝辅食, Baby Supplemental Food) by using them in posts unrelated to their literal meaning. The hashtags were strategically chosen from topics that would be uninteresting to the male audience they wanted to block. Through a mixed-methods approach, we analyzed the practice of hashtag re-appropriation based on 5,800 collected posts and interviewed 24 active users from diverse backgrounds to uncover users' motivations and reactions towards the re-appropriation. This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures.

受賞
Honorable Mention
著者
Ruyuan Wan
Pennsylvania State University, State College, Pennsylvania, United States
Lingbo Tong
University of Notre Dame, South Bend, Indiana, United States
Tiffany Knearem
Google, San Francisco, California, United States
Toby Jia-Jun. Li
University of Notre Dame, Notre Dame, Indiana, United States
Ting-Hao Kenneth. Huang
Pennsylvania State University, University Park , Pennsylvania, United States
Qunfang Wu
Harvard University, Cambridge, Massachusetts, United States
DOI

10.1145/3706598.3713379

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713379

動画