RecommenderSystems

🔅 جدیدترین مقالات منتشر شده در موضوع ریکامندر سیستم ها در ماه فوریه 2024 #تازه_ها #تازه #جدید #جدیدترین #جدیدترینها #فوریه #آخرین #مقالات #مقاله #مقاله_پایه #New #Paper #February #Alerts #ScholarAlerts #Research #Search #Scholar #GoogleScholar @Recommender_Systems

🔅 جدیدترین مقالات منتشر شده در موضوع ریکامندر سیستم ها در ماه ژانویه 2024 #تازه_ها #تازه #جدید #جدیدترین #جدیدترینها #ژانویه #آخرین #مقالات #مقاله #مقاله_پایه #New #Paper #January #Alerts #ScholarAlerts #Research #Search #Scholar #GoogleScholar @Recommender_Systems

🔅 کیفیت سنجی مقالات در هنگام جستجو در گوگل اسکالر با این افزونه کروم: آدرس وبسایت افزونه: 🔗 https://www.excitation.tech/ #افزونه #مرورگر #کروم #استناد #اعتبار #اسکالر #مقاله #Extension #Research #Tools #ResearchTools #Research_Tools #Chrome #Tool #Search #Free #Ranking #Rankings #Check #Results #Result #Cited #CitedBy #Paper @Recommender_Systems

دسترسی رایگان به هوش مصنوعی های پولی : 🔗 https://start.chatgot.io/ برای استفاده از هر هوش مصنوعی یک @ تایپ کنید و نام هوش مصنوعی را در ادامه بیاورید ✅ بهترین های هوش مصنوعی برای پژوهشگران ▫️لیستی از جایگزین های رایگان برای ChatGPT ▫️کاربردهای هوش مصنوعی در پژوهش #هوش_مصنوعی #پژوهش #ابزار #ابزار_پژوهش #AI #ChatGPT #Research #Perplexity #perplexity.ai #Copilot #ChatGPT4 #Research #Tools #ResearchTools #Research_Tools #AI_Tools #Chat #GPT #Best #BestTools #Best_Tools #Chat #GPT #Free @Recommender_Systems

2024 Poisoning Federated Recommender Systems with Fake Users 🔗 https://arxiv.org/pdf/2402.11637.pdf Abstract : Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are particularly notable among user-side attacks, as participants upload malicious model updates to deceive the global model, often intending to promote or demote specific targeted items. This study investigates strategies for executing promotion attacks in federated recommender systems. Current poisoning attacks on federated recommender systems often rely on additional information, such as the local training data of genuine users or item popularity. However, such information is challenging for the potential attacker to obtain. Thus, there is a need to develop an attack that requires no extra information apart from item embeddings obtained from the server. In this paper, we introduce a novel fake user based poisoning attack named PoisonFRS to promote the attacker-chosen targeted item in federated recommender systems without requiring knowledge about user-item rating data, user attributes, or the aggregation rule used by the server. Extensive experiments on multiple real-world datasets demonstrate that PoisonFRS can effectively promote the attacker-chosen targeted item to a large portion of genuine users and outperform current benchmarks that rely on additional information about the system. We further observe that the model updates from both genuine and fake users are indistinguishable within the latent space. #Poisoning #Federated #Fake #Users #User #Malicious #Attack #Novel #PoisonFRS @Recommender_Systems

2024 Foundation Models for Recommender Systems: A Survey and New Perspectives 🔗 https://arxiv.org/pdf/2402.11143.pdf Abstract: Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs). In this paper, we attempt to thoroughly examine FMbased recommendation systems (FM4RecSys). We start by reviewing the research background of FM4RecSys. Then, we provide a systematic taxonomy of existing FM4RecSys research works, which can be divided into four different parts including data characteristics, representation learning, model type, and downstream tasks. Within each part, we review the key recent research developments, outlining the representative models and discussing their characteristics. Moreover, we elaborate on the open problems and opportunities of FM4RecSys aiming to shed light on future research directions in this area. In conclusion, we recap our findings and discuss the emerging trends in this field. #Foundation #Models #Survey #New #Perspectives #Model #Perspective #FMs #FM4RecSys @Recommender_Systems

🔅 لیست عالی از دروس مقدماتی و پیشرفته یادگیری ماشین که در یوتیوب به صورت رایگان قابل دسترس هستند 🔗 https://github.com/dair-ai/ML-YouTube-Courses ✅ At DAIR.AI we open AI education. In this repo, we index and organize some of the best and most recent machine learning courses available on YouTube. - Andrew Ng CS229 ML: https://lnkd.in/gkDEyuCS - MIT: Deep Learning for Art: https://lnkd.in/grusgt3Z - Stanford CS230: Deep Learning: https://lnkd.in/ggXNEX7K - Practical Deep Learning for Coders: https://lnkd.in/giHMNrHG, https://lnkd.in/gDtRtHmG - Stanford CS224W: Machine Learning with Graphs: https://lnkd.in/grZC_j4N - Probabilistic Machine Learning: https://lnkd.in/gjSpNDCD - MIT 6.S191: Introduction to Deep Learning: https://lnkd.in/gWtSdkSH - UC Berkeley CS 182: Deep Learning: https://lnkd.in/gzHS6m8G - UC Berkeley Deep Unsupervised Learning: https://lnkd.in/gPdPbKku - Yann Lecun's NYU Deep Learning SP21L: https://lnkd.in/gdyzmf8b - Stanford CS25 - Transformers United: https://lnkd.in/gaZVn3wY - Hugging Face NLP Course: https://lnkd.in/gigfE2Yj - Stanford CS224N: Natural Language Processing with Deep Learning: https://lnkd.in/g4fg4_wX - CMU Neural Nets for NLP: https://lnkd.in/gVpUwtXE - Stanford CS224U: Natural Language Understanding: https://lnkd.in/gMeGkkzV - CMU Advanced NLP: https://lnkd.in/gAtrsGqY - CMU Multilingual NLP: https://lnkd.in/ghbcWftV - Stanford CS231N: Convolutional Neural Networks for Visual Recognition: https://lnkd.in/g3DeCWEc - Michigan Deep Learning for Computer Vision: https://lnkd.in/gbdgGgJQ - AMMI Geometric Deep Learning Course: https://lnkd.in/gYH6Vuum - UC Berkeley CS 285 Deep Reinforcement Learning: https://lnkd.in/gH-HYdqz - Intro to Deep Learning and Generative Models: https://lnkd.in/gxuTtkSk - Stanford CS330: Deep Multi-Task and Meta Learning: https://lnkd.in/gasntdBh #Courses #AI #YouTube #NLP #MachineLearning #ML #Machine_Learning #Repo #gitHub #Best @Recommender_Systems

2024 Causal Learning for Trustworthy Recommender Systems: A Survey 🔗https://arxiv.org/pdf/2402.08241.pdf Abstract: Recommender Systems (RS) have significantly advanced online content discovery and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite numerous progress on TRS, most of them focus on data correlations while overlooking the fundamental causal nature in recommendation. This drawback hinders TRS from identifying the cause in addressing trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noises while offering insightful explanations for TRS. However, there lacks a timely survey in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field. #Causal #Learning #Trustworthy #Survey #Causality #TRS #CTRS @Recommender_Systems

🔅 لیست دروس و فیلم های آموزشی دانشگاه فردوسی مشهد 🔗 http://kfe.um.ac.ir/ سامانه فیلم های آموزشی دانشگاه فردوسی مشهد، به منظور به اشتراک گذاری فیلم های آموزشی مربوط به دروس این دانشگاه که توسط اساتید دانشگاه فردوسی تدریس شده است، ایجاد گردیده است #مشهد #دانشگاه #فیلم #آموزش #یادگیری #درس #مجازی ▫️فیلم های کلاس درس "یادگیری عمیق" دکتر سید کمال الدین غیاثی دانشگاه فردوسی مشهد ▫️دروس دانشگاه صنعتی شریف #OCW #UM #OpenCourseWare #Open_CourseWare #Open #Course #Ware #Tutorials #Video #Learn #Tutorial #Free #Online #Courses #virtual #Mashhad @Recommender_Systems

2024 An improved sequential recommendation model based on spatial self-attention mechanism and meta learning 🔗 https://link.springer.com/article/10.1007/s11042-023-17948-5 Abstract: Sequential recommendation systems in cold-start scenarios aim to provide recommendations as accurately as possible for users with sparse behavior, which is a challenging issue in this field. Recently, meta-learning algorithms have been introduced into the cold-start recommendations and have obtained some better results. However, most of these meta-learning-based methods require auxiliary information or knowledge from other fields, and do not model the third-order relationship between users and their sequential interactive items, which could result in models lacking the ability to capture dynamic transitions of user preferences. In addition, the traditional meta-learning-based methods ignore the diversity of user preferences, which limits the performance improvement in sequential recommendation systems in the cold-start scenarios. To address the above problems, a new sequential recommendation model is proposed for cold-start scenarios which combines meta-learning and attention mechanism. In the proposed model, a third-order interaction modeling paradigm of “Item-User-Item” (IUI) is proposed firstly to obtain dynamic transitions of user preferences in the item space. Then, an embedding strategy is used to embed the user behavior sequences and transition information of user preferences from the training task into a spatial self-attentive (SS) recommendation model. Moreover, a meta-learning-based parameter training approach is presented, where a task processor (TP) is designed to improve the universality of parameter initialization. At last, some experiments are conducted on three real-world datasets, and the evaluation metrics HR@10 and NDCG@10 of the proposed model improve by about 5.1% and 5.6% compared with the baseline, respectively. The experimental results show that the proposed model in this paper achieves the best recommendation results in comparison with existing models. #شروع_سرد #ColdStart #Cold_Start #Cold #MetaLearning #Meta_Learning #Meta #Learning #Attention #Transitions #Transition #User #Preferences #Preference #TransitionsUserPreferences #Transitions_of_User_Preferences #Sequential @Recommender_Systems