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检索条件"任意字段=Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization"

共 94 条记录，以下是71-80 订阅

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De-Anchor: Mitigating Attention Polarization for Lifelong user Behavior modeling in Click-Through Rate Prediction 24

De-Anchor: Mitigating Attention Polarization for Lifelong Us...

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33rd Companion of the acm World Wide Web conference, WWW 2023

作者： Liu, Hongzun Yin, Kang Sun, Tianyu Huang, Rui Li, Yunsong Fang, Xiao Liu, Zhaojie Liu, Weidong Zhou, Guorui Tsinghua University Beijing China Kuaishou Technology Beijing China

ISBN: (纸本)9798400701726

user lifelong behavior sequences are essential for click-through rate (CTR) prediction tasks in industrial recommender systems. Attention-based module, especially multi-head target attention (MHTA), has been proven to be effective in aggregating behavior features given a certain target item. However, we found a common phenomenon that attention weights in MHTA tend to over-concentrate on merely a small subset of a user’s historical behaviors, producing a sparse one-hot distributed attention weights in training gradually, which we call Attention Polarization (AP). These polarized weights on certain behaviors (which we call "attention anchor") could make the model fail to capture a user’s diversified interests, and harm the learning of behavior embedding as the gradients on these features are nearly zero. We introduce two indicators: anchor rate and attention entropy to measure the magnitude of AP, and propose De-Anchor, a novel method to alleviate it, which can serve as a stand-alone and parameter-efficient plug-in to existing CTR backbones. De-Anchor contains two modules: Anchor-aware gradient dropout (AGD) forcing the model to capture diversified interest information from behavior sequences by discarding gradients of non-behavior features, and Target-aware attention anchor (TAA) providing a pseudo behavior to offload excessive weights of MHTA. Extensive offline experiments and industrial online A/B tests demonstrate the efficacy of our method. © 2024 Copyright held by the owner/author(s).

关键词： Recommender systems

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Walking with PACE - Personalized and Automated Coaching Engine 22

Walking with PACE - Personalized and Automated Coaching Engi...

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30th acm conference on user modeling, adaptation and personalization (UMAP)

作者： Vardhan, Madhurima Hegde, Narayan Merugu, Srujana Prabhat, Shantanu Nathani, Deepak Seneviratne, Martin Muhammad, Nur Reddy, Pranay Lakshminarasimhan, Sriram Singh, Rahul Lorenzana, Karina Motwani, Eshan Talukdar, Partha Raghuveer, Aravindan Google Bengaluru India Google London England Google Singapore Singapore Google Mountain View CA USA

ISBN: (纸本)9781450392075

We design and implement a personalized and automated physical activity coaching engine, PACE, which uses the Fogg's behavioral model (FBM) to engage users in mini-conversation based coaching sessions. It is a chat-based nudge assistant that can boost (encourage) and sense (ask) the motivation, ability and propensity of users to walk and help them in achieving their step count targets, similar to a human coach. We demonstrate the feasibility, effectiveness and acceptability of PACE by directly comparing to human coaches in a Wizard-of-Oz deployment study with 33 participants over 21 days. We tracked coach-participant conversations, step counts and qualitative survey feedback. Our findings indicate that the PACE framework strongly emulated human coaching with no significant differences in the overall number of active days, step count and engagement patterns. The qualitative user feedback suggests that PACE cultivated a coach-like experience, offering barrier resolution via motivational and educational support. We use traditional human-computer interaction approaches, to interrogate the conversational data and report positive PACE-participant interaction patterns with respect to addressal, disclosure, collaborative target settings, and reflexivity. As a post-hoc analysis, we annotated the conversation logs from the human coaching arm and trained machine learning (ML) models on these data sets to predict the next boost (AUC 0.73 +/- 0.02) and sense (AUC 0.83 +/- 0.01) action. In future, such ML-based models could be made increasingly personalized and adaptive based on user behaviors.

关键词： Automated assistant fitness coaching personalization behavior science persuasive models

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Context-based Fast Recommendation Strategy for Long user Behavior Sequence in Meituan Waimai 24

Context-based Fast Recommendation Strategy for Long User Beh...

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33rd Companion of the acm World Wide Web conference, WWW 2023

作者： Feng, Zhichao Xie, JunJie Li, Kaiyuan Qin, Yu Wang, Pengfei Li, Qianzhong Yin, Bin Li, Xiang Lin, Wei Wang, Shangguang Beijing University of Posts and Telecommunications Beijing China Meituan Beijing China

ISBN: (纸本)9798400701726

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. A number of existing sequential recommendation models struggle to capture long-term dependencies, or they exhibit high complexity, both of which make it difficult to satisfy the unique business requirements of Meituan Waimai’s recommender system. To better model user interests, we consider selecting relevant sub-sequences from users’ extensive historical behaviors based on their preferences. In this specific scenario, we’ve noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy (referred to as CoFARS) to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding Points of Interest (PoIs) based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ Jensen–Shannon(JS) divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. Subsequently, we construct a temporal graph that encompasses both prototype and context nodes to integrate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai’s display recommender system, leading to a 4.6% surge in CTR a

关键词： Recommender systems

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user modeling Meets Research Integrity: Challenges in Translating AI-powered Rehabilitation Systems into Regulated Clinical Practice: user modeling Meets Research Integrity 25

User Modeling Meets Research Integrity: Challenges in Transl...

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Adjunct proceedings of the 33rd acm conference on user modeling, adaptation and personalization

作者： Ilaria Bortone Feliciana Catino Giuseppe Colacicco Rodolfo Sardone Giuseppe Campanile APS Public Health Environment and Social Equity - PLANET Bari Italy MedITech 4.0 Competence Center Projects Unit Bari Italy University of Bari Dept. of Computer Science Bari Italy Comune di Taranto Taranto Italy University of Bari Bari Italy Dept. of Translational Biomedicine and Neuroscience (DiBraiN) Bari Italy Azienda Sanitaria Locale di Taranto Urban Health Center Taranto Italy University of Liverpool Liverpool United Kingdom Azienda Sanitaria Locale di Bari Bari Italy LUMSA University Department of Law Economics Politics and Modern languages Taranto Italy

ISBN: (纸本)9798400713996

Artificial intelligence (AI) is increasingly embedded in rehabilitation technologies designed for children with developmental disorders, offering new opportunities for personalised, adaptive care. However, the translation of these systems from lab to clinic is often decisively shaped by regulatory frameworks such as the EU General Data Protection Regulation (GDPR), the Medical Device Regulation (MDR), and the forthcoming AI Act. This position paper explores how these three regulatory pillars influence the ethical deployment of AI and the design and innovation process behind pediatric rehabilitation tools. Drawing from recent literature and ongoing policy developments, we argue that GDPR, MDR, and the AI Act should not be viewed merely as compliance hurdles but as co-design forces that enable trustworthy, interpretable, and clinically viable AI. We propose a forward-looking framework to align innovation with regulation to facilitate the safe and effective implementation of AI-powered rehabilitation in child-centred healthcare.

关键词： Developmental Disorders

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Enhancing Sense of Attention from a Communication Robot by Drawing the user's Face on Its Thought Bubble in the Video Conferencing System 21

Enhancing Sense of Attention from a Communication Robot by D...

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9th International conference on user modeling, adaptation and personalization Human-Agent Interaction (HAI)

作者： Nitada, Yuta Yoshikawa, Yuichiro Meneses, Alexis Ishiguro, Hiroshi Osaka Univ Osaka Japan

ISBN: (纸本)9781450386203

In recent years, web conferencing systems have become widely used in non-face-to-face interactions. However, web conferencing systems make difficult to establish eye contact for users and can lead to decrease in non-verbal communication. In this paper, we propose a method that will enhance a user's feeling of interlocutor's attention, even when the line of sight does not actually match, by displaying a thought bubble for the interlocutor filled with the user's in-camera image. Because robots have the same gaze problem as humanswe thought it is important to knowthat thisweb conference system can represent the attention of a simple moving robot without using complex gaze mechanisms. So, we conducted a within-subject design experiment using a robot whose gaze and movements can be controlled and a participant as interlocutors in a web conference with and without the thought bubble. The experimental results indicate that our proposed method increased the sense of being looked at.

关键词： Video conferencing system Thought bubble Visual field Sense of being looked at

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FairUMAP 2020: The 3rd Workshop on Fairness in user modeling, adaptation and personalization 20

FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling...

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proceedings of the 28th acm conference on user modeling, adaptation and personalization (UMAP)

作者： Mobasher, Bamshad Kleanthous, Styliani Ekstrand, Michael Berendt, Bettina Otterbacher, Jahna Tal, Avital Shulner Depaul Univ Chicago IL 60604 USA OUC Cyprus Ctr Algorithm Transparency Nicosia Cyprus Boise State Univ People & Informat Res Team Boise ID 83725 USA TU Berlin Berlin Germany Katholieke Univ Leuven Leuven Belgium Univ Haifa Haifa Israel

The 3rd FairUMAP workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on the one hand, and bias, fairness and transparency in algorithmic systems on the ot... 详细信息

ISBN: (纸本)9781450368612

关键词： Algorithmic fairness Algorithmic bias user modeling personalization adaptation transparency explainability

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modeling Expertise in Assistive Navigation Interfaces for Blind People 18

Modeling Expertise in Assistive Navigation Interfaces for Bl...

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23rd International conference on Intelligent user Interfaces (IUI)

作者： Ohn-Bar, Eshed Guerreiro, Joao Ahmetovic, Dragan Kitani, Kris M. Asakawa, Chieko Carnegie Mellon Univ Pittsburgh PA 15213 USA IBM Res Tokyo Tokyo Japan

ISBN: (纸本)9781450349451

Evaluating the impact of expertise and route knowledge on task performance can guide the design of intelligent and adaptive navigation interfaces. Expertise has been relatively unexplored in the context of assistive indoor navigation interfaces for blind people. To quantify the complex relationship between the user's walking patterns, route learning, and adaptation to the interface, we conducted a study with 8 blind participants. The participants repeated a set of navigation tasks while using a smartphone-based turn-by-turn navigation guidance app. The results demonstrate the gradual evolution of user skill and knowledge throughout the route repetitions, significantly impacting the task completion time. In addition to the exploratory analysis, we take a step towards tailoring the navigation interface to the user's needs by proposing a personalized recurrent neural net work-based behavior model for expertise level classification.

关键词： blind navigation knowledge experience interface adaptation user modeling personalization recurrent neural networks

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acm International conference Proceeding Series

ACM International Conference Proceeding Series

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33rd Computer Graphics International conference, CGI 2016

ISBN: (纸本)9781450341233

The proceedings contain 30 papers. The topics discussed include: total least square error computation in E2: a new simple, fast and robust algorithm;a real-time depth map refinement and disparity ranges expansion system (DrdE) for multiview rendering;paint with stitches: a random-needle embroidery rendering method;interactive dissection of digital organs based on metaballs;what is in a rumor: combined visual analysis of rumor flow and user activity;dynamic gesture recognition with Laban movement analysis and hidden Markov models;high-dimensional visual similarity search: k-d generalized randomized forests;efficient screenspace rendering for area lights;temporal coherent video decolorization using proximity optimization;3D architectural modeling: coarse-to-fine model fitting on point cloud;and an intuitive sketch-based transfer function design via contextual and regional labeling.

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Effects of Language modeling and its personalization on Touchscreen Typing Performance 15

Effects of Language Modeling and its Personalization on Touc...

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33rd Annual CHI conference on Human Factors in Computing Systems (CHI)

作者： Fowler, Andrew Partridge, Kurt Chelba, Ciprian Bi, Xiaojun Ouyang, Tom Zhai, Shumin Oregon Hlth & Sci Univ Portland OR 97201 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781450331456

Modern smartphones correct typing errors and learn user-specific words (such as proper names). Both techniques are useful, yet little has been published about their technical specifics and concrete benefits. One reason is that typing accuracy is difficult to measure empirically on a large scale. We describe a closed-loop, smart touch keyboard (STK) evaluation system that we have implemented to solve this problem. It includes a principled typing simulator for generating human-like noisy touch input, a simple-yet-effective decoder for reconstructing typed words from such spatial data, a large web-scale background language model (LM), and a method for incorporating LM personalization. Using the Enron email corpus as a personalization test set, we show for the first time at this scale that a combined spatial/language model reduces word error rate from a pre-model baseline of 38.4% down to 5.7%, and that LM personalization can improve this further to 4.6%.

关键词： Mobile text entry keyboard error correction language modeling

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Design and adaptation for cross-device, context-dependent user interfaces 33

Design and adaptation for cross-device, context-dependent us...

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33rd Annual CHI conference on Human Factors in Computing Systems, CHI EA 2015

作者： Paternò, Fabio CNR-ISTI HIIS Laboratory Via Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9781450331463

This tutorial aims to help user interface designers and developers to understand the issues involved in multi-device, context-dependent interactive applications, which can be accessed through wearable, mobile and stationary devices even exploiting different interaction modalities. It will provide a discussion of the possible solutions in terms of concepts, techniques, languages, and tools, with particular attention to Web environments. The tutorial will deal with the various strategies in order to adapt, distribute, and migrate the user interface according to the context of use.

关键词： user interfaces

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