RecommenderSystems

2024 Progress in Privacy Protection: A Review of Privacy Preserving Techniques in Recommender Systems, Edge Computing, and Cloud Computing 🔗 https://arxiv.org/pdf/2401.11305.pdf As digital technology evolves, the increasing use of connected devices brings both challenges and opportunities in the areas of mobile crowdsourcing, edge computing, and recommender systems. This survey focuses on these dynamic fields, emphasizing the critical need for privacy protection in our increasingly data-oriented world. It explores the latest trends in these interconnected areas, with a special emphasis on privacy and data security. Our method involves an in-depth analysis of various academic works, which helps us to gain a comprehensive understanding of these sectors and their shifting focus towards privacy concerns. We present new insights and marks a significant advancement in addressing privacy issues within these technologies. The survey is a valuable resource for researchers, industry practitioners, and policy makers, offering an extensive overview of these fields and their related privacy challenges, catering to a wide audience in the modern digital era. #Privacy #Review #Preserving #Techniques #Edge #EdgeComputing #Edge_Computing #Cloud #Computing #CloudComputing #Cloud_Computing #Security #Location #Models #LargeLanguageModels #Large_Language_Models #Large #Language #Models #LLM #LLMs #Mobile #Survey #Trends #Comprehensive #Insights @Recommender_Systems

2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 🔗 https://www.computer.org/csdl/proceedings-article/seaa/2023/423500z023/1TlXBUQzis8 DOI Bookmark: 10.1109/SEAA60479.2023.00009 #Conference #SEAA #Proceeding #Proceedings #IEEE @Recommender_Systems

🔅 مقالات پولی سایت https://medium.com را در کادر سایت زیر وارد کرده و آن را دریافت کنید: https://readmedium.com/ همچنین می توانید افزونه زیر را بر مرورگر نصب کنید تا بدون محدودیت و رایگان مطالب این سایت را مطالعه کنید: ▫️ افزونه برای کروم: https://chrome.google.com/webstore/detail/medium-parser/egejbknaophaadmhijkepokfchkbnelc?hl=en&authuser=0 ▫️ افزونه برای فایرفاکس: https://addons.mozilla.org/en-US/firefox/addon/medium-parser/ #افزونه #مرورگر #کروم #فایرفاکس #مدیوم #Extension #Research #Tools #ResearchTools #Research_Tools #Chrome #Tool #Search #Medium #FireFox #Free @Recommender_Systems

🔅 Introduction to Data Science Data Wrangling and Visualization with R 🔗 https://rafalab.dfci.harvard.edu/dsbook-part-1/https://rafalab.dfci.harvard.edu/dsbook-part-1/ Advanced Data Science: 🔗 http://rafalab.dfci.harvard.edu/dsbook-part-2/ This book started out as the class notes used in the HarvardX Data Science Series #دانشمند_داده This book is dedicated to all the people involved in building and maintaining R and the R packages we use in this book. https://download.library.lol/main/2519000/276727cd0f1f6fea60002e6d839d370a/Rafael%20A.%20Irizarry%20-%20Introduction%20to%20Data%20Science%20Data%20Analysis%20and%20Prediction%20Algorithms%20with%20R-CRC%20Press%20%282019%29.pdf #علم_داده #Introduction #DataScience #Data_Science #DS #DataWrangling #Visualization #Wrangling #Advanced @Recommender_Systems

🔅Why-Not Explainable Graph Recommender 🔗 https://hal.science/hal-04364920/document ABSTRACT: Explainable Recommendation Systems (RS) enhance the user experience on online platforms by recommending personalized content, as well as explanations for the given recommendations to add transparency and build up trust in the platforms. Extending the notion of explainable RS, in this paper we define Why-Not explanations for recommendations that were expected but not returned, and propose and implement a technique for computing Why-Not explanations in a post-hoc manner for a graph-based RS. Our approach builds on the notion of counterfactual explanations in the means of a set of user-rooted edges to add or remove in the graph, in order to place the missing recommendation to the top of the recommendation list, and provides in this way actionable insights on the source data and their interrelations. Our experimental evaluation on a real-world data set demonstrates the feasibility of our proposal and reveals interesting directions for future work. #Explainable #Graph #Recommender #Explanations #Questions #AI #WhyNot @Recommender_Systems

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🔅افزونه Rapid Journal Quality Check در کروم اعتبار هر مقاله در سرچ‌های گوگل اسکالر را تعیین می‌کند: If you're searching for academic journals on Google Scholar, but wish you had instant access to their rankings. Try, the Rapid Journal Quality Check Chrome extension. This handy tool displays a journal's rankings right next to your search results. It works seamlessly with Google Scholar, including scholar profile pages and subpages like "cited by," and more! 🔗 https://chromewebstore.google.com/detail/rapid-journal-quality-che/mfkbhgdamgfcifnhcdebfahkgnbkagmo 🔅با نصب افزونه CatalyzeX در مرورگر کروم می توان کدهای پیاده سازی شده مقالات را پیدا کرد. افزونه با جستجوی عنوان موضوع مورد نظر در گوگل اسکالر، ArXiv و ... مقالاتی که کدهای برنامه نویسی آن در گیت هاب موجود باشند را مشخص می کند. با کلیک روی کلمه CODE صفحه کد مقاله نشان داده می شود #افزونه #مرورگر #کروم #استناد #اعتبار #کد #اسکالر #مقاله #گیت #CODE #Extension #SaveTime #Ranking #Rankings #Check #Research #Tools #ResearchTools #Research_Tools #Chrome #Quality #Handy #Tool #Search #Results #Result #Cited #CitedBy #Paper #Git #GitHub @Recommender_Systems

🔅 500 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲 🔗 https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code 500 AI Machine learning Deep learning Computer vision NLP Projects with code !! 🔅 آموزش یادگیری ماشین با کد 🔅 مقاله با کد 🔅 بهترین مقالات هوش مصنوعی با کد و شرح #یادگیری_ماشین #گیت_هاب #ماشین_لرنینگ #کد #پروژه #هوش_مصنوعی #AI #MachineLearning #DeepLearning #NLP #Projects #Code #ML #DL #GitHub #Papers #Machine_Learning #Tutorial #Tutorials #Best #OpenSourceProjects #Open_Source_Projects @Recommender_Systems

🔅 گزارش مقالات ریترکت شده 🔗 https://retractionwatch.com/ #Retract #Retraction #Tracking #Plagiarism #Checker #Check #Detector @Recommender_Systems

🔅 نقشه کامل راه یادگیری علم داده در سال 2024 Perfect Roadmap To Learn Data Science In 2024 این نقشه جامع شامل آموزش از صفر پایتون، یادگیری ماشین، یادگیری عمیق، MLOPS, LLM, Generative AI و بسیاری موارد دیگر است 🔗 https://github.com/krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2024 #علم_داده #Perfect #Roadmap #DataScience #Data_Science #DS @Recommender_Systems

🔅 جدیدترین مقالات منتشر شده در موضوع ریکامندر سیستم ها در ماه دسامبر #تازه_ها #تازه #جدید #جدیدترین #جدیدترینها #دسامبر #آخرین #مقالات #مقاله #مقاله_پایه #New #Paper #December #Alerts #ScholarAlerts #Research #Search #Scholar #GoogleScholar @Recommender_Systems

Recommender Systems: Techniques Effects and Measures Toward Pluralism and Fairness 2024 Introduction to Digital Humanism: A Textbook 🔗 https://books.google.com/books?id=8nnqEAAAQBAJ&newbks=0&printsec=frontcover&pg=PA416&hl=en&source=newbks_fb#v=onepage&q&f=false #Techniques #Effects #Measures #Pluralism #Fairness #Springer @Recommender_Systems

2023 User response modeling in recommender systems: a survey 🔗 https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=znsl&paperid=7438&option_lang=eng Abstract: Over the last several decades, recommender systems have become an integral part of both our daily lives and the research frontier at machine learning. In this survey, we explore various approaches to developing simulators for recommendation systems, especially for modeling the user response function. We consider simple probabilistic models, approaches based on generative adversarial networks, and full-scale simulators, and also review the datasets available for the research community. #User #Response #Modeling #Survey #Adversarial #Learning #AdversarialLearning #Adversarial_Learning #Synthetic #Data #Simulator #Probabilistic #Models @Recommender_Systems

2023 Large Language Models are Not Stable Recommender Systems 🔗 https://arxiv.org/pdf/2312.15746.pdf Abstract: With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel recommender systems. However, we have observed that directly using LLMs as a recommender system is usually unstable due to its inherent position bias. To this end, we introduce exploratory research and find consistent patterns of positional bias in LLMs that influence the performance of recommendation across a range of scenarios. Then, we propose a Bayesian probabilistic framework, STELLA (Stable LLM for Recommendation), which involves a two-stage pipeline. During the first probing stage, we identify patterns in a transition matrix using a probing detection dataset. And in the second recommendation stage, a Bayesian strategy is employed to adjust the biased output of LLMs with an entropy indicator. Therefore, our framework can capitalize on existing pattern information to calibrate instability of LLMs, and enhance recommendation performance. Finally, extensive experiments clearly validate the effectiveness of our framework. #LargeLanguageModels #Large_Language_Models #Large #Language #Models #LLM #LLMs #Stable #NLP #Bayesian #Probabilistic #Framework #STELLA @Recommender_Systems

2023 Recommender systems: models, challenges and opportunities 🔗 https://scholar.google.com/scholar_url?url=https://hait.od.ua/index.php/journal/article/download/190/224&hl=en&sa=X&d=15511728973549314577&ei=It2PZfCLCveE6rQPtL-PqA8&scisig=AFWwaeZHf83PSuPQj2CSNtSDBxzO&oi=scholaralrt&hist=FJ-P8gQAAAAJ:12188588115516513205:AFWwaeY1Hb4FiJRHAuE9X0oKmfSc&html=&pos=3&folt=rel&fols= Abstract: The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used. #Models #Challenges #Opportunities #Review #Comprehensive @Recommender_Systems

🔅 The AI tool cheat sheet that covers everything from text to design. Dive into the possibilities: ✅ For text geniuses, LLMs like ChatGPT and Claude are at your service ✅ Visual creators can explore Midjourney or DALL-E for images that captivate ✅ Video virtuosos, have you tried Runway for editing that’s a cut above? ✅ Audiophiles, Murf and Eleven Labs can make your words sing ✅ Marketers, Jasper and SEMrush are your new best friends ✅ And for the productivity pros, Taskade and Notion AI are here to streamline #AI #CheatSheets #Cheat_Sheets #CheatSheet #Cheat_Sheet #Cheat #Sheet #Sheets #Tools #AI_Tools #AiTools #Best #BestTools #Best_Tools @Recommender_Systems