Article
Computer Science, Artificial Intelligence
Pengyu Xu, Mingxuan Xia, Lin Xiao, Huafeng Liu, Bing Liu, Liping Jing, Jian Yu
Summary: Tag recommendation improves the quality of information retrieval services by assisting users in tagging. However, existing studies rarely consider the long-tail distribution of tags and the topic-tag correlation. This paper proposes a Topic-Guided Tag Recommendation (TGTR) model that incorporates dynamic neural topics to recommend tags and balances the effects of topics and tags. Experimental results show that our model outperforms state-of-the-art approaches, especially on tail-tags.
Article
Chemistry, Multidisciplinary
Yi Zuo, Shengzong Liu, Yun Zhou, Huanhua Liu
Summary: A social tagging system improves recommendation performance by utilizing tags as auxiliary information, which are text descriptions provided by individual users. However, there are challenges such as data sparsity, ambiguity, and difficulty in capturing multi-aspect user interests and item characteristics from these tags. To address these issues, a tag-aware recommendation model based on attention learning is proposed to capture diverse potential features for users and items.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Junqi Zhang, Yiqun Liu, Jiaxin Mao, Weizhi Ma, Jiazheng Xu, Shaoping Ma, Qi Tian
Summary: This article presents two different simulation environments for offline training of the RL ranking agent: the Context-aware Click Simulator (CCS) and the Fine-grained User Behavior Simulator with GAN (UserGAN). Based on the simulation environment, a User Behavior Simulation for Reinforcement Learning (UBS4RL) re-ranking framework is designed, consisting of three modules: a feature extractor for heterogeneous search results, a user simulator for collecting simulated user feedback, and a ranking agent for generation of optimized result lists.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ahtsham Manzoor, Dietmar Jannach
Summary: Conversational recommender systems have gained significant attention, particularly with the use of neural models for end-to-end learning. Current generation-based systems face challenges in generating appropriate and grammatically correct responses. This study re-assesses the potential of retrieval-based approaches and introduces a novel technique for response retrieval and ranking. A user study demonstrates that the responses generated by the proposed system outperform two recent generation-based systems.
INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Alfan Farizki Wicaksono, Alistair Moffat
Summary: This paper introduces a session-based offline evaluation framework for measuring the overall usefulness of search sessions. By modeling data from two commercial search engines, the user conditional continuation probability and user conditional reformulation probability are proposed to develop new metrics that show greater correlation with observed user behavior during search sessions.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Ben Wang, Jiqun Liu
Summary: Pre-adoption expectations play a crucial role in users' evaluation of information systems. However, research on users' search expectations and their connections to tasks, experiences, and behaviors in online information search is lacking. To address this gap, we conducted a controlled-lab Web search study and collected direct feedback on users' expected information gains and search efforts. Our results show that users' expectations are significantly influenced by task characteristics and search experience. Furthermore, user expectations are closely linked to browsing behaviors and search satisfaction. Understanding user expectations can enhance our understanding of search behavioral patterns and evaluation of interaction experience, and inform the design and evaluation of expectation-aware information retrieval systems.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Jia Xu, Hongming Zhang, Xin Wang, Pin Lv
Summary: To address the problem of user cold-start recommendation, a novel adaptive meta-learning model based on user relevance (AdaML) is proposed. This model identifies related users with similar preferences and utilizes their information to improve user cold-start recommendations.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dario Garigliotti, Krisztian Balog, Katja Hose, Johannes Bjerva
Summary: In this article, the authors propose a method of recommending specific tasks to users based on their search queries. By combining traditional term-based ranking techniques with continuous semantic representations, the proposed method outperforms text-based baselines in recommending tasks such as planning a holiday trip or organizing a party. The study also includes an analysis of features and queries.
NATURAL LANGUAGE ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Angelo Cesar Mendes da Silva, Diego Furtado Silva, Ricardo Marcondes Marcacini
Summary: This paper introduces a new method for learning multimodal representations by constructing representations that combine different musical features and explore similarity simultaneously. The proposed method achieves the best results in comparative evaluation and highlights the discriminative power of multimodality in musical representations.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Maxwell J. Yin, Boyu Wang, Charles Ling
Summary: In the era of rapid paper publications, automatic citation recommendations are highly useful. Previous methods were time-consuming and lacked diversity in recommendations, but our model achieves faster and more diverse recommendations by mapping citation contexts and cited papers to the same vector space and utilizing a multi-group contrastive learning method.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Tianyi Liu, Yusuke Sugano
Summary: This paper introduces a method for efficient model personalization on a small interactive object recognition camera device by combining sample recommendations with an IML workflow. The proposed method involves interactive training of a noise filter and providing ternary feedback, resulting in more efficient model training and improved system usability.
Article
Computer Science, Theory & Methods
Ye Tao, Can Wang, Alan Wee-Chung Liew
Summary: The traditional ensemble-based recommendation systems rely on user meta-data to determine the weight distribution of base recommenders. To address the limited user information in real-world scenarios, we propose AIRE, a user-agnostic ensemble model that learns the representation of base recommenders from interactions history. AIRE outperforms single session-based recommenders and effectively distinguishes input sequences and base recommenders. This work highlights the importance of building representations for base recommenders in ensemble-based recommendation systems and opens up new avenues for future research.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Education & Educational Research
Sidra Tahir, Yaser Hafeez, Muhammad Azeem Abbas, Asif Nawaz, Bushra Hamid
Summary: In response to the difficulty of retrieving learning objects in Technology Enhanced Learning, this research proposes an innovative machine learning and filter based context-aware recommender system. It helps course designers easily search and access diverse and semantically related learning objects. The proposed model demonstrates remarkable performance and achieves higher accuracy compared to a common search engine.
EDUCATION AND INFORMATION TECHNOLOGIES
(2022)
Article
Computer Science, Information Systems
Kelsey Urgo, Jaime Arguello
Summary: This paper provides a systematic review of different types of assessments used in search-as-learning studies to date. It discusses the potential benefits and drawbacks of these assessments, explores assessments used outside of search-as-learning, and offers recommendations for future research.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Naushad Alam, Yvette Graham
Summary: In this paper, we provide a detailed description of our lifelog retrieval system Memento, which participated in the 2021 Lifelog Search Challenge. Memento uses semantic representations of images and textual queries to bridge the semantic gap between visual scenes and user information needs. The system also includes a minimalist user interface with features like visual data filtering and temporal search. Additionally, we present a comparative analysis of Memento's performance at LSC 2021 and suggest improvements for future iterations.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Psychiatry
Maia Jacobs, Melanie F. Pradier, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, Krzysztof Z. Gajos
Summary: The study found that interacting with machine learning recommendations did not significantly improve clinicians' treatment selection accuracy, and interacting with incorrect recommendations paired with limited but easily interpretable explanations led to a significant reduction in accuracy. Incorrect ML recommendations may adversely impact clinician treatment selections, and explanations alone are not enough to address overreliance on imperfect ML algorithms.
TRANSLATIONAL PSYCHIATRY
(2021)
Article
Computer Science, Information Systems
Tung Vuong, Salvatore Andolina, Giulio Jacucci, Tuukka Ruotsalo
Summary: This study investigates the use of spoken input from conversations as a context to improve query auto-completion for web searches. The research shows the advantage of combining spoken conversational context with web-search context for improved retrieval performance, suggesting that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2021)
Article
Neurosciences
Nergis C. Khan, Vineet Pandey, Krzysztof Z. Gajos, Anoopum S. Gupta
Summary: This study found that children with A-T were less active in terms of high intensity movements and had a narrower range of activity intensities compared to controls. Activity metrics derived from a wrist sensor were strongly correlated with clinical severity and specific motor features, demonstrating high reliability. These findings suggest that wrist sensors can provide accurate and reliable information about motor performance in A-T children.
Article
Computer Science, Information Systems
Tung Vuong, Salvatore Andolina, Giulio Jacucci, Tuukka Ruotsalo
Summary: The effect of contextual information obtained from a user's digital trace on Web search performance is studied. Contextual information is modeled using Dirichlet-Hawkes processes (DHP) and used to augment Web search queries. A field study was conducted with participants installing a screen recording and digital activity monitoring system on their laptops to collect data on Web search queries and associated context. The results show that incorporating more contextual information significantly improves Web search rankings.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Chen He, Luana Micallef, Baris Serim, Tung Vuong, Tuukka Ruotsalo, Giulio Jacucci
Summary: Exploratory search involves browsing, learning, and formulating new search targets. To support such dynamic search behavior, this study proposes the use of interactive visual facets (IVF) as a tool for user comprehension and control of the information space. By addressing specific design requirements and introducing novel concepts, the IVF tool demonstrates its effectiveness in supporting exploratory search.
JOURNAL OF VISUALIZATION
(2022)
Article
Health Care Sciences & Services
Jody L. Lin, Bernd Huber, Ofra Amir, Sebastian Gehrmann, Kimberly S. Ramirez, Kimberly M. Ochoa, Steven M. Asch, Krzysztof Z. Gajos, Barbara J. Grosz, Lee M. Sanders
Summary: This study aimed to assess the feasibility and acceptability of GoalKeeper (GK), an internet-based system for eliciting and monitoring family-centered goals for children with medical complexity (CMC). The results showed that family-centered technologies like GK are feasible and acceptable for the care of CMC, but integrating them into electronic health records remains a key challenge.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Vineet Pandey, Nergis C. Khan, Anoopum S. Gupta, Krzysztof Z. Gajos
Summary: This study explores the accuracy and reliability of using Hevelius, an active digital phenotyping system, for assessing motor impairments in individuals at home. The results show that consecutive sessions at home can produce accurate and reliable assessments, and these assessments have good test-retest reliability when aggregated over multiple sessions.
ACM TRANSACTIONS ON ACCESSIBLE COMPUTING
(2023)
Article
Psychology, Biological
Carlos de la Torre-Ortiz, Michiel Spape, Tuukka Ruotsalo
Summary: Visual recognition requires inferring the similarity between a perceived object and a mental target. This study redefines similarity using a generative adversarial neural network (GAN) and shows that the P300 amplitude is correlated with the distance-to-target. The study demonstrates that the P300 indexes the distance between perceived and target image in smooth, natural, and complex visual stimuli.
Article
Computer Science, Artificial Intelligence
Michiel Spape, Keith M. M. Davis III, Lauri Kangassalo, Niklas Ravaja, Zania Sovijarvi-Spape, Tuukka Ruotsalo
Summary: While it is difficult to explain the exact definition of personal attraction, it depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs) combined with brain-computer interfaces provide a way to model subjective preferences unconstrained by pre-defined model parameterization. Through an experiment, it was found that using electroencephalography (EEG) to control a GAN produces highly accurate, individually attractive images.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Imtiaj Ahmed, Ville J. Harjunen, Giulio Jacucci, Niklas Ravaja, Tuukka Ruotsalo, Michiel M. M. Spape
Summary: The study explored how emotional expressions of virtual agents influence the way humans touch them. The results showed that people's emotional perception and experience affect the intensity and duration of touch towards virtual agents, suggesting that haptic responses can serve as an implicit measure of people's experience with their virtual companions.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Keith M. Davis, Carlos de la Torre-Ortiz, Tuukka Ruotsalo
Summary: This study proposes a novel approach that utilizes brain responses as a supervision signal for learning semantic feature representations. By recording participants' brain responses while they view facial images, researchers are able to learn the latent space of a generative adversarial network (GAN) that can be used to edit semantic features of new images. The experiments demonstrate that implicit brain supervision achieves a comparable semantic image editing performance to explicit manual labeling.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Tung Vuong, Salvatore Andolina, Giulio Jacucci, Pedram Daee, Khalil Klouche, Mats Sjoberg, Tuukka Ruotsalo, Samuel Kaski
Summary: The system, EntityBot, captures user context across application boundaries to recommend relevant information entities for current tasks. By continuously monitoring digital activity, it effectively detects task context and retrieves entities, leading to high user satisfaction in real-world tasks.
15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Keith M. Davis, Michiel Spape, Tuukka Ruotsalo
Summary: By using brain-computer interfacing to infer preferences directly from the human brain, a new possibility for collaborative filtering recommendation systems is introduced. Through experimentation, it was demonstrated that brain-computer interfacing can serve as a viable alternative for behavioral and self-reported preferences, with broad implications for practical applications.
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
(2021)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)
