RecommenderSystems

🔅 پیشرفت های هوش مصنوعی در سال 2023 #ArtificialIntelligence #Artificial_Intelligence #AI #Tools #Trends #Models @Recommender_Systems

🔅 Artificial Intelligence for Beginners - A Curriculum 🔗 https://github.com/microsoft/AI-For-Beginners Explore the world of Artificial Intelligence (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!" ❗️ Other Curricula ▫️ AI for Beginners ▫️ Data Science for Beginners ▫️ Generative AI for Beginners ▫️ Web Dev for Beginners ▫️ IoT for Beginners ▫️ Machine Learning for Beginners ▫️ XR Development for Beginners ▫️ Mastering GitHub Copilot for AI Paired Programming #ArtificialIntelligence #Artificial_Intelligence #AI #Beginners #Curriculum #Beginner #Microsoft #GitHub #Curricula @Recommender_Systems

2023 On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm 🔗 https://arxiv.org/pdf/2312.10864.pdf Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry. However, traditional cloud-based RSs face inevitable challenges, such as resource-intensive computation, reliance on network access, and privacy breaches. In response, a new paradigm called on-device recommender systems (ODRSs) has emerged recently in various industries like Taobao, Google, and Kuaishou. ODRSs unleash the computational capacity of user devices with lightweight recommendation models tailored for resource constrained environments, enabling real-time inference with users’ local data. This tutorial aims to systematically introduce methodologies of ODRSs, including (1) an overview of existing research on ODRSs; (2) a comprehensive taxonomy of ODRSs, where the core technical content to be covered span across three major ODRS research directions, including on-device deployment and inference, on-device training, and privacy/security of ODRSs; (3) limitations and future directions of ODRSs. This tutorial expects to lay the foundation and spark new insights for follow-up research and applications concerning this new recommendation paradigm. ACM Reference Format: Hongzhi Yin, Tong Chen, Liang Qu, and Bin Cui. 2024. On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm. In Proceedings of The Web Conference 2024. ACM, New York, NY, USA, 4 pages. https://doi.org/TBD #OnDevice #OnDeviceLearning #On_Device_Learning #On_Device #Tutorial #New #Generation #NewGeneration #New_Generation #Paradigm #Federated #FederatedLearning #Federated_Learning #FL #Privacy #Security #ODRSs #Comprehensive #Taxonomy #ACM @Recommender_Systems

2023 Data Scarcity in Recommendation Systems: A Survey 🔗 https://arxiv.org/pdf/2312.10073.pdf The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations. Despite their significance, data scarcity issues have significantly impaired the effectiveness of existing RS models and hindered their progress. To address this challenge, the concept of knowledge transfer, particularly from external sources like pre-trained language models, emerges as a potential solution to alleviate data scarcity and enhance RS development. However, the practice of knowledge transfer in RSs is intricate. Transferring knowledge between domains introduces data disparities, and the application of knowledge transfer in complex RS scenarios can yield negative consequences if not carefully designed. Therefore, this article contributes to this discourse by addressing the implications of data scarcity on RSs and introducing various strategies, such as data augmentation, self-supervised learning, transfer learning, broad learning, and knowledge graph utilization, to mitigate this challenge. Furthermore, it delves into the challenges and future direction within the RS domain, offering insights that are poised to facilitate the development and implementation of robust RSs, particularly when confronted with data scarcity. We aim to provide valuable guidance and inspiration for researchers and practitioners, ultimately driving advancements in the field of RS. ACM Reference Format: Zefeng Chen, Wensheng Gan, Jiayang Wu, Kaixia Hu, and Hong Lin. 2023. Data Scarcity in Recommendation Systems: A Survey. J. ACM 1, 1 (December 2023), 32 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn #DataScarcity #Data_Scarcity #Data #Scarcity #Survey #Fusion #LargeModel #Large_Model #Large #Model #TransferringKnowledge #Transferring_Knowledge #ACM @Recommender_Systems

🔅 مدل های زبان بزرگ Large Language Models LLM مانند GPT: Generative Pre Trained Transformer ChatGPT چگونه کار می کنند؟ #LargeLanguageModels #Large_Language_Models #LargeLanguageModel #Large_Language_Model #LLM #NLP #GPT #ChatGPT #Model #Models @Recommender_Systems

🔅 NC State Theses and Dissertations 🔅 دانلود پایان نامه های دانشگاه کارولینای شمالی در مقطع ارشد و دکترا در همه رشته ها Permanent URI for this community 🔗 https://www.lib.ncsu.edu/resolver/1840.20/23 ▫️ موتور جستجو رایگان برای دانلود مقاله و پایان‌نامه ▫️جستجوی رایگان و دانلود پایان نامه ها و مقالات خارجی ▫️معرفی چند سایت برای دانلود پایان نامه های دانشگاه های اروپایی و آمریکایی ▫️ معرفی چند سایت‌ برای دسترسی رایگان به منابع علمی #پایان_نامه #ارشد #دکترا #دانلود #Theses #Dissertations #Community #Repository #Download #Thesis #Theses #Dissertation #Search #Free #Research #Tools #ResearchTools #Research_Tools @Recommender_Systems

Data Science with Python Workflow #DataScience #DS #Data_Science #Python #Workflow @Recommender_Systems

2023 Embedding Compression in Recommender Systems: A Survey 🔗 https://dl.acm.org/doi/pdf/10.1145/3637841 To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information iltering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued embeddings. However, the embedding tables are huge and account for most of the parameters in industrial-scale recommender systems. In order to reduce memory costs and improve eiciency, various approaches are proposed to compress the embedding tables. In this survey, we provide a comprehensive review of embedding compression approaches in recommender systems. We first introduce deep learning recommendation models and the basic concept of embedding compression in recommender systems. Subsequently, we systematically organize existing approaches into three categories, namely low-precision, mixed-dimension, and weight-sharing, respectively. Lastly, we summarize the survey with some general suggestions and provide future prospects for this field. #Embedding #EmbeddingTables #Embedding_Tables #Tables #Compression #Survey #Model #Comprehensive #DL #DeepLearning @Recommender_Systems

🔅 Data Science RoadMap #DataScience #DS #RoadMap #Data #Science #Data_Science @Recommender_Systems

🔅 The Data Engineering Handbook ❗️ This repo has all the resources you need to become an amazing data engineer! ▫️ Make sure to check out the projects section for more hands-on examples! ▫️ Make sure to check out the interviews section for more advice on how to pass data engineering interviews! 🔗 https://github.com/DataEngineer-io/data-engineer-handbook ❗️ A public GitHub repo with all the resources, books, companies and social media accounts you should be following to stay current on data engineering topics. ✅ Resources: Great books - Communities - Companies - Data Engineering blogs of companies - Data Engineering Whitepapers - Great YouTube Channels - Great Podcasts - Newsletters - LinkedIn - Twitter / X - Instagram - TikTok - Design Patterns - Courses / Academies - Certifications Courses - Conferences #GitHub #Repo #Resources #Resource #Data_Engineering #Topics #DataEngineering #Data #Engineering #Handbook #Books #Projects #Interviews #Project #Interview @Recommender_Systems

🔅AI Tools Directory and List of Best Free AI by Category (Top 10) | Aixploria The largest list of AI tools available on the web 🔗 https://www.aixploria.com/en/ این وب سایت #هوش_مصنوعی های جدید را به صورت دسته بندی شده موضوعی معرفی می کند #AI #Tools #Best #Free #Category #Top #Aixploria #List @Recommender_Systems

2023 Recommender Systems in E-Commerce: Performance Trade-Offs Between Collaborative Filtering and Matrix Factorization 🔗 https://jespublication.com/uploads/2023-V14I1088.pdf ABSTRACT In the dynamic landscape of e-commerce, where user preferences are as diverse as the products themselves, the quest for an optimal recommender system is a perpetual journey. This study comprehensively explores the performance trade-offs between Collaborative Filtering (CF) and Matrix Factorization (MF), shedding light on their nuanced strengths and limitations. CF, known for its competitive accuracy, excels at aligning recommendations with user preferences but grapples with scalability and adaptability challenges as datasets grow and user behavior evolves. In contrast, MF proves a robust alternative, gracefully handling data sparsity and changing user preferences while maintaining scalability. The choice between CF and MF should be tailored to the specific needs of an e-commerce platform, whether startup or established marketplace. Additionally, the study delves into hybrid approaches, where the fusion of CF and MF achieves superior performance, harmonizing accuracy and scalability. In this ever-evolving e-commerce landscape, remember that the compass is not as crucial as the journey itself, with the true North being personalized user experiences guided by the winds of data-driven decision-making. #eCommerce #Performance #Trade_Offs #TradeOffs #Collaborative #MatrixFactorization #Matrix_Factorization #Matrix #Factorization #CF #MF #Hybrid @Recommender_Systems

2023 Sports Recommender Systems: Overview and Research Issues 🔗 https://arxiv.org/pdf/2312.03785.pdf Abstract: Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues. Keywords: Recommender Systems, Sports, Collaborative Filtering, Content based Filtering, Knowledge-based Recommenders, Group Recommenders #Sport #Overview #Research #Issues #Sports #Issue #Group #Collaborative #Content #Knowledge #eSport #Overview @Recommender_Systems

🔅 Python Roadmap #Roadmap #Learn #Python #Path @Recommender_Systems

🔅 Use this Roadmap to Learn Python Are you starting to learn Python? This comprehensive roadmap will guide you through a versatile career path. Remember, each journey is unique, and this roadmap is adaptable to fit your personal goal! 🔹 Establish the Foundation: Begin with the essentials to build a strong base: - Basic Syntax: Dive into Python's unique language structure. - Variables & Data Types: Discover the various ways to store data. - Conditionals: Learn to steer your code's decision-making. - Data Collections (Lists, Tuples, Sets, Dicts): Master the art of data manipulation. - Functions & Built-in Functions: Embrace the power of modular, reusable code. - Type Casting & Exception Handling: Navigate through data type conversions and errors with finesse. 🔹 Core Concepts - Data Structures & Algorithms: Delve deeper into the heart of programming: - Arrays & Linked Lists: Organize data elements effectively. - Heaps, Stacks, Queues: Explore specialized data structures. - Hash Tables: Unlock the efficiency of key-value pair storage. - Binary Search Trees: Optimize data navigation and organization. - Recursion: Tackle complex problems with self-referential functions. - Sorting Algorithms: Get a grip on vital sorting methods. 🔹 Advanced Python Mastery: Raise the bar with sophisticated topics: - Regular Expressions (RegEx): Master text manipulation through pattern matching. - Lambdas: Simplify your code with anonymous functions. - Object-Oriented Programming (Classes & Inheritance): Craft structured, reusable code. - Methods & Dunder Methods: Boost class functionalities. - Package Management (PyPI, Pip, Conda): Handle libraries and dependencies like a pro. - Efficient Coding Techniques: Embrace the art of List Comprehensions & Generator Expressions. - Diverse Programming Paradigms: Explore different coding approaches. - Iterators (Built-in & Custom): Streamline data navigation. 🔹 Framework Proficiency: Expand your toolkit with popular Python frameworks: - Web Development (Pyramid, Flask, Django): Create dynamic web applications. - Concurrency (Synchronous & Asynchronous Programming): Manage multiple tasks simultaneously. - Asynchronous Frameworks (Tornado, aiohttp, gevent, Sanic, Fast API): Discover a range of options for asynchronous programming. 🔹 Quality Assurance - Test Your Creations: Guarantee the excellence of your applications with reliable testing tools: - Testing Suites (doctest, nose, pytest, unittest/pyUnit): Ensure your code performs flawlessly. #Roadmap #Learn #Python #Beginner #Advanced #Path #Comprehensive @Recommender_Systems

🔅 پیاده سازی نظریه بازی ها: https://hamed.github.io/trust/ #نظریه_بازی #GameTheory #Game_Theory #Trust #Game #GitHub @Recommender_Systems