RecommenderSystems

🔅 Data Science Public DataSets ❗️مطالب مربوط به دیتاست در کانال ▫️2023 RecBaselines2023 a new dataset for choosing baselines for recommender models ▫️2023 ITM-Rec An Open Data Set for Educational Recommender Systems ▫️The Linked Open Data Cloud ▫️The 50 Best Free Datasets for Machine Learning ▫️A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19 ▫️Breast Cancer Wisconsin (Diagnostic) Data SetPredict whether the cancer is benign or malignant ▫️Recommendation and Ratings Public Data Sets ▫️3 Awesome Visualization Techniques for every dataset 🔅انواع دیتاست 🔅دیتاست های سیستم های پیشنهاد دهنده ▫️۱۰ سایت که می توان دیتای پژوهشی را در آنها جستجو کرد ▫️دیتاست ۳۳۰۰۰ صفحه ocr فارسی اَرشاسب ▫️بیش از 4000 دیتاست به همراه مقالاتی که بر مبنای آنها تدوین شده اند ▫️مجموعه کامل پیکره گان کشور ▫️دیتاست های جدید از حجم 255 مگابایت تا 22 ترابایت به بخش "داده های آماده" مرجع هدوپ ایران اضافه شد ▫️دیتاست‌های مختلف برای زبان فارسی ، جمع‌آوری شده در یک سایت ▫️کتاب داده کاوی دیتاست های حجیم و کلان ▫️با این ابزار رایگان و متن باز کار با انواع دیتاست ها راحت‌تر شده و به راحتی می توان آنها را دانلود کرد ▫️این ابزار پایتونی کمک می کند تا با کشیدن روی صفحه ، دیتاست تستی مورد نظر خود را درست کنید ▫️دیتاست ها و درسها و مقالات آموزشی در زمینه های مختلف مثل یادگیری ماشین و داده کاوی و .. ▫️مجموعه داده یا دیتاست چت گروه های تلگرامی درباره بورس تهران ▫️ دیتاست های مختلف برای کارهای یادگیری ماشین و داده کاوی ▫️دیتاست های رایگان استنفورد برای کارهای مرتبط با علوم داده و شبکه های پیچیده و پویا ▫️گوگل ابزار آنلاینی برای جستجو بین 25 میلیون دیتاست ارائه کرده است ▫️دیتا‌سِت فیلم‌های ایرانی پلتفرم فیلیمو، بر سایت کگل قرار گرفت ▫️برخی از داده های پزشکی منتشر شده در سامانه دیتاپول #دیتاست #دیتا #DataScience #Public #DataSets #Data_Sets #DataSet #Data_Set #Data #OpenData #Open_Data @Recommender_Systems

🔅 The Best AI Tools & Applications Online Find the best AI tools for any task with the largest AI database online. We collect and organize over 9284 of the best AI tools & applications into one easy to reference platform. 🔗 https://bestofai.com #Best #AI #Tools #Applications #Online #Platform @Recommender_Systems

2023 Feature-level Deeper Self-Attention Network with Contrastive Learning for Sequential Recommendation Abstract — Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. Meanwhile, the item-level sequential recommendation also suffers from limited supervised signal issues. In this paper, we propose a novel model Feature-level Deeper Self-Attention Network with Contrastive Learning (FDSA-CL) for sequential recommendation. Specifically, FDSA-CL first integrates various heterogeneous features of items into feature-level sequences with different weights through a vanilla attention mechanism. After that, FDSA-CL applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Moreover, we propose contrastive learning and item feature recommendation tasks to capture the embedding commonality and further utilize the beneficial interaction among the two levels, so as to alleviate the sparsity of the supervised signal and extract the most critical information. Finally, we jointly optimize the above tasks. We evaluate the proposed model using two real-world datasets and experimental results show that our model significantly outperforms the state-of-the-art approaches. #ItemLevel #Item_Level #Item #Level #Patterns #FeatureLevel #Feature_Level #Feature #SelfAttention #Self_Attention #Self #Attention #Contrastive #Sequential #Novel #Model #FDSACL #Heterogeneous #Features #State_of_the_Art #StateoftheArt @Recommender_Systems

🔅Working of Machine Learning Model #Working #MachineLearning #Model #ML #Machine_Learning @Recommender_Systems

🔅 Python Tutorials یادگیری و آموزش پایتون: ▫️Roadmap to Learn Python ▫️2014 The Python Workbook ▫️Python Tutorial Codes ▫️The Real Python Developer Road-map ▫️What Python Can Do ▫️2019 Python Crash Course ▫️Deitel developer Series2019 Python for Programmers ▫️2018 Modern Python Standard Library Cookbook ▫️2016 Python 201 ▫️Python Object Oriented Programming ▫️2018 Python for dummies ▫️01 - فیلم های آموزشی پایتون به فارسی ▫️کتاب حل 650 مثال و تمرین برنامه نویسی پایتون ▫️ده دوره آموزشی پایتون با اخذ مدرک #یادگیری #پایتون #کد #آموزش #Python #Tutorial #Codes #Learn #Tutorials #Workbook #Coding #Roadmap #PythonProgramming #Deitel #Programmer #Developer #Udemy #Object_Oriented #Programming #Video @Recommender_Systems

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

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

🔅 Use this Roadmap to Learn Python: Beginner to Advanced! Navigating the Python universe can be daunting, but with the right roadmap, your path to proficiency becomes clear. #Roadmap #Learn #Python #Beginner #Advanced #Path @Recommender_Systems

🔅Visual explanations of core machine learning concepts ▫️مفاهیم یادگیری ماشین با انیمیشن Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application. As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner. 🔗 https://mlu-explain.github.io/ #یادگیری_ماشین #آموزش #یادگیری #ML #MachineLearning #Machine_Learning #Visual #Teach #MLU @Recommender_Systems

🔅 گنجینه علم داده برای دانشمند داده شامل: Job Board: Job Openings - Interships- Traineeships Events: Conferences - Meetups - Webinars Courses: Training Courses - Workshops - Study Programs - Study Circles Challenges: Competitions - Hackathons 🔗 https://aigents.co/ We are one of the 🌎 worlds' largest community networks for Data Scientists, Machine Learning Engineers, and Software Developers. Combined these communities count for more than 5 million members. Since our 🚀 launch we helped thousands of professionals to teach themselves new skills, to land their dream job and to bring their careers to the next level. #DataScientist #Data_Scientist #DS #ML #MachineLearning #Machine_Learning #Tutorials #CaseStudies #Case_Studies #Career #Job #JobBoard #JobOpenings #Internships #Traineeships #Events #Conferences #Meetups #Webinars #Courses #Training #Workshops #Challenges #Competitions #Hackathons @Recommender_Systems

2023 Tag2Seq: Enhancing Session-Based Recommender Systems with Tag-Based LSTM https://doi.org/10.1007/978-3-031-48316-5_37 Abstract A key problem of session-based recommendation (sequence pattern or neighborhood-based recommendation) is to predict items of interest based on anonymous user’s current interaction session or unavailable profile user. While recurrent neural networks (RNNs) have been applied to the recommender systems with remarkable success, little attention has been paid to the semantic and contextual information about tags. In this work we argue that long-short term memory recurrent neural network (LSTM-RNN) incorporating pre-trained global vectors embeddings can be applied to capture semantic and contextual information about tags in session-based recommendation systems with remarkable results. Therefore, this paper introduces Tag2Seq, a novel recommendation system based on a recurrent neural network architecture that utilizes LSTM to leverage user’s tags. Our method is evaluated on the Movielens 20M dataset, Experimental results indicate that Tag2Seq outperforms state-of-the-art session-based recommendation methods on large real-world dataset. #Tag2Seq #Enhancing #Session #SessionBased #Session_Based #Tag #TagBased #Tag_Based #LSTM #RNN #GloVe #DL #DeepLearning #Deep_Learning #Movielens #State_of_the_Art #StateoftheArt @Recommender_Systems