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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation Systems with Neural Collaborative Filtering and Sentiment Analysis

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SN Computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. Distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. This paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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Joint advertising and ordering decisions under the pre-sales crowdfunding

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Management System Engineering 2023年第1期2卷 1-14页

作者： Zeng, Kuan Shen, Yaohan School of Management Wuhan Textile University Wuhan China Research Center of Enterprise Decision Support Key Research Institute of Humanities and Social Sciences Wuhan China School of Management Wuhan University of Technology Wuhan China Research Center for Data Science and Intelligent Decision Making Wuhan University of Technology Wuhan China

Our study considers a company launching new product on pre-sales crowdfunding platform and explores the joint advertising and ordering decisions in mass market thereafter. Under the “AON” mechanism, we construct profit-maximizing models to examine the impact of pledged amount on the company’s decision on advertising expense, order quantity and funding target. The results show that the optimal order quantity increases with the pledged amount in crowdfunding, and the company won’t promote new product in mass market until the pledged amount reaches a funding threshold, and the advertising expense increases with the pledged amount since then. In addition, we figure out the feasible scope for the funding target and suggest the company set the target within the scope, to ensure the project profitability. Furthermore, we demonstrate that the expected pledged amount won’t increase with the funding target, and the optimal funding target would be the lower bound in its feasible scope, to improve the campaign success. Finally, we conduct numerical analysis to verify our theoretical results.

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Syllables to Scenes: Literary-Guided Free-Viewpoint 3D Scene Synthesis from Japanese Haiku

arXiv

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arXiv 2025年

作者： Yu, Chunan Han, Yidong Ding, Chaotao Zang, Ying Zhu, Lanyun Chen, Xinhao Li, Zejian Xu, Renjun Chen, Tianrun School of Information Engineering Huzhou University China College of Computer Science and Technology Zhejiang University China College of Software Engineering Zhejiang University China Center for Data Science Zhejiang University China Technology Co. LTD China Singapore University of Technology and Design Singapore School of Humanities Wenzhou University China

In the era of the metaverse, where immersive technologies redefine human experiences, translating abstract literary concepts into navigable 3D environments presents a fundamental challenge in preserving semantic and emotional fidelity. This research introduces HaikuVerse, a novel framework for transforming poetic abstraction into spatial representation, with Japanese Haiku serving as an ideal test case due to its sophisticated encapsulation of profound emotions and imagery within minimal text. While existing text-to-3D methods struggle with nuanced interpretations, we present a literary-guided approach that synergizes traditional poetry analysis with advanced generative technologies. Our framework centers on two key innovations: (1) Hierarchical Literary-Criticism Theory Grounded Parsing (H-LCTGP), which captures both explicit imagery and implicit emotional resonance through structured semantic decomposition, and (2) Progressive Dimensional Synthesis (PDS), a multi-stage pipeline that systematically transforms poetic elements into coherent 3D scenes through sequential diffusion processes, geometric optimization, and real-time enhancement. Extensive experiments demonstrate that HaikuVerse significantly outperforms conventional text-to-3D approaches in both literary fidelity and visual quality, establishing a new paradigm for preserving cultural heritage in immersive digital spaces. Project website at: https://***/ Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Pilgrimage to Pureland: Art, Perception and the Wutai Mural VR Reconstruction

arXiv

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arXiv 2023年

作者： Mu, Rongxuan Nie, Yuhe Cao, Kent You, Ruoxin Wei, Yinzong Tong, Xin Architecture Tianjin University Tianjin China Computer Science and Technology Southern University of Science and Technology Guangdong China Division of Arts and Humanities Duke Kunshan University Duke University United States Arts and Humanities Duke Kunshan University Jiangsu China Chinese Literature Wuhan University Wuhan China Data Science Research Center Duke Kunshan University Jiangsu China

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided Pilgrimage To Pureland VR reconstruction of Dunhuang Mogao Grottoes to offer the general public an accessible and engaging way to explore the Dunhuang murals. We put forward an immersive VR reconstruction paradigm that can efficiently convert complex 2D artwork into a VR environment. We reconstructed the Mt. Wutai pilgrimage mural in Cave 61, Mogao Grottoes, Dunhuang, into an immersive VR environment and created a plot-based and interactive experience that offers users a more accessible solution to visit, understand and appreciate the complex religious, historical, and artistic value of Dunhuang murals. Our system remarkably smoothed users' approaches to those elusive cultural heritages. Appropriate adaptation of plots and 3D VR transfer consistent with the original art style could enhance the accessibility of cultural heritages. © 2023, CC BY-NC-SA.

关键词： Caves

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Value Identification in Multistakeholder Recommender Systems for humanities and Historical Research: The Case of the Digital Archive ***

arXiv

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arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in ***, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context. © 2024, CC BY-SA.

关键词： Embedded systems

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Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution

arXiv

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arXiv 2025年

作者： Kim, Jin Lee, Byunghwee You, Taekho Yun, Jinhyuk Department of Intelligent Semiconductors Soongsil University Seoul06978 Korea Republic of School of Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute for Social Data Science Pohang University of Science and Technology Pohang37673 Korea Republic of Center for Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of AI Convergence Soongsil University Seoul06978 Korea Republic of

The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subject). Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements. Additionally, using contextual keywords extracted from paintings, we show how artistic expression evolves alongside societal changes. Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks, highlighting the significance of mutual interaction between society and art. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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EvaHan2023: Overview of the First International Ancient Chinese Translation Bakeoff 1

EvaHan2023: Overview of the First International Ancient Chin...

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1st Workshop on Ancient Language Translation, ALT 2023, co-located with 19th Machine Translation Summit, MTS 2023

作者： Wang, Dongbo Lin, Litao Zhao, Zhixiao Ye, Wenhao Meng, Kai Sun, Wenlong Zhao, Lianzhen Zhao, Xue Shen, Si Zhang, Wei Li, Bin College of Information Management Nanjing Agricultural University Nanjing210031 China School of Marxism Nanjing Agricultural University Nanjing210095 China School of Foreign Languages Nanjing Tech University Nanjing211816 China School of Foreign Languages China Pharmaceutical University 211198 China School of Economics & Management Nanjing University of Science and Technology 210094 China School of Chinese Language and Literature Center of Language Big Data and Computational Humanities Nanjing Normal University 210097 China

This paper presents the results of the First International Ancient Chinese Transalation Bakeoff (EvaHan), which is a shared task of the Ancient Language Translation Workshop (ALT2023) and a co-located event of the 19th Machine Translation Summit 2023 (MTS 2023). We described the motivation for having an international shared contest, as well as the datasets and modalities. The contest consists of two modalities, closed and open. In the closed modality, the participants are only allowed to use the training data, and the participating teams achieved the highest BLEU scores of 27.33 and 1.11 in the tasks of translating ancient Chinese to Modern Chinese and translating Ancient Chinese to English, respectively. In the open mode, contestants can use any available data and models. The participating teams achieved the highest BLEU scores of 29.68 and 6.55 in the Ancient Chinese to Modern Chinese and Ancient Chinese to English tasks, respectively. © 2023 The Authors.

关键词： Machine translation

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CHALLENGES IN IMPLEMENTING A RECOMMENDER SYSTEM FOR HISTORICAL RESEARCH IN THE humanities

arXiv

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arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Trattner, Christoph Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria University of Bergen & Media Futures Bergen Norway

This extended abstract describes the challenges in implementing recommender systems for digital archives in the humanities, focusing on ***, a platform for historical legal documents. We discuss three key aspects: (i) the unique characteristics of so-called charters as items for recommendation, (ii) the complex multi-stakeholder environment, and (iii) the distinct information-seeking behavior of scholars in the humanities. By examining these factors, we aim to contribute to the development of more effective and tailored recommender systems for (digital) humanities research. © 2024, CC BY-SA.

关键词： Recommender systems

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Regional profile of questionable publishing

arXiv

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arXiv 2023年

作者： You, Taekho Park, Jinseo Lee, June Young Yun, Jinhyuk Institute for Social Data Science Pohang University of Science and Technology 77 Cheongam-ro Gyeongsangbukdo Pohang37673 Korea Republic of Center for Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology 192 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Center for Global R&D Data Analysis Korea Institute of Science and Technology Information 66 Heogi-ro Dongdaemun-gu Seoul02456 Korea Republic of School of AI Convergence Soongsil University 369 Sangdo-ro Dongjak-gu Seoul06978 Korea Republic of

Countries and authors in the academic periphery occasionally have been criticized for contributing to the expansion of questionable publishing because they share a major fraction of papers in questionable journals. On the other side, topics preferred by mainstream journals sometimes necessitate large-scale investigation, which is impossible for developing countries. Thus, local journals, commonly low-impacted, are essential to sustain the regional academia for such countries. In this study, we perform an in-depth analysis of the distribution of questionable publications and journals with their interplay with countries quantifying the influence of questionable publications regarding academia’s inequality. We find that low-impact journals play a vital role in the regional academic environment, whereas questionable journals with equivalent impact publish papers from all over the world, both geographically and academically. The business model of questionable journals differs from that of regional journals, and may thus be detrimental to the broader academic community. © 2023, CC BY.

关键词： Developing countries

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