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2022 Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets Nowadays, the sequential information, i.e., the ordering of the recorded feedback, is one of the most frequently used auxiliary information for developing recommendation algo-rithms. However, there are still some challenges in collaborative filtering with sequentialimplicitfeedback.Foronething,theremaybesomeco-occurrenceand‘‘leap-occurrence” phenomena in users’ behavior sequences. For another, a weak point of utilizing implicit feedback is that the unobserved behaviors do not always indicate ‘‘dislike”. In this paper, we focus on these two fundamental challenges in users’ preference learning, i.e., the uncertainty of the ordering of the next items and the uneven quality of the negative sam-ples, and propose a novel solution named COFIS (short for collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets). Specifically,our COFIS incorporates two learning paradigms (i.e., pairwise and pointwise) and two expression strategies (i.e., ‘‘MOO” and ‘‘MOS”, representing two different ways to treat the negative item-sets when compared with the positive item-sets). Our COFIS can be applied to existing methods such as many factorization- and deep learning-baseed models.We derive four specific algorithms of our COFIS based on Fossil (denoted as COFIS(-,-) for short) in particular, and further show the results of our COFIS-improved methods (includ-ing the factorization-based COFIS, and the deep learning-based SASRec (COFIS) and FMLP-Rec(COFIS)). Notice that we treat the seminal works and their pointwise version as special cases of our COFIS. Empirical studies on four real-world datasets show the superiority of our proposed COFIS compared with the state-of-the-art baselines. Moreover, we also study the impact of some key parameters in our COFIS. Keywords: Collaborative filtering Sequential implicit feedback Learning preferences over item-sets Factored item similarity models Factored Markov chains #Collaborative #Filtering #CollaborativeFiltering #Collaborative_Filtering #Sequential #Implicit #Feedback #Learning #Users #Preferences #ItemSets #Markov #Factored #SASRec #COFIS @Recommender_Systems

🔅 گنجینه علوم داده 2019 Data Science from Scratch First Principles with Python Joel Grus O'Reilly Second Edition 2018 Data Science from Scratch 2023 Data science for next-generation recommender systems 2020 Principles of Data Science Data Science Full Archive Introduction to Data Science Data Science with Python Workflow Data Science RoadMap Data Science Public DataSets 2017 Python Data Science Handbook 2019 Data Science Concepts and Practice Data Science Pocket Dictionary The Data Science Lifecycle 2022 Data Science and Innovations for Intelligent Systems 200+ Python & Data Science Tips The Complete Collection of Data Science Books 2021 Data Science Cheat Sheet 2.0 2020 The Big Book of Data Science Use Cases 6 Books to Help You Learn Data Science ▫️دسترسی به بیش از 2000 دوره، جزوه، ... رایگان «علم داده» در یک سایت ▫️یک دانشگاهِ رایگانِ «علم داده» در یک سایت ▫️نقشه کامل راه یادگیری علم داده در سال 2024 https://www.oreilly.com/library/view/data-science-from/9781492041122/ ISBN: 9781492041139 #DS #DataScience #Data_Science #Data #Science #Scratch #Python #OReilly #Best کتاب best seller آمازون #علم_داده #گنجینه @Recommender_Systems

𝗛𝗼𝘄 𝘁𝗼 𝗢𝘂𝘁𝗹𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗣𝗵.𝗗.𝗧𝗵𝗲𝘀𝗶𝘀 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀: 𝗔 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀' 𝗚𝘂𝗶𝗱𝗲! Outlining the chapters of your PhD thesis is a vital step in organizing your research and presenting it effectively. Here is a straightforward guide to help you outline your thesis chapters: 1-7 1. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 • Provide an overview of your research topic and its significance. • State the research problem or question. • Outline the objectives and scope of your study. • Introduce the structure of your thesis. 2. 𝗟𝗶𝘁𝗲𝗿𝗮𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 • Review relevant literature and theoretical frameworks. • Identify key concepts, theories, and previous research. • Highlight gaps in the existing literature that your study addresses. • Discuss the relevance of previous studies to your research. 3. 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 • Describe your research design and approach. • Explain data collection methods and tools. • Discuss data analysis techniques. • Address any ethical considerations. 4. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 • Present your findings in a clear and organized manner. • Use tables, figures, and charts to illustrate your results. • Provide detailed descriptions and interpretations of the data. 5. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 • Interpret your results in relation to your research question. • Discuss the implications of your findings. • Compare your results with previous research. • Address limitations and suggest areas for future research. 6. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 • Summarize the main findings of your study. • Reflect on the significance of your research. • Discuss the contributions to the field. • Offer concluding remarks and suggestions for further study. 7. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 - List all sources cited in your thesis according to the required citation style. 𝗔𝗽𝗽𝗲𝗻𝗱𝗶𝗰𝗲𝘀 (if applicable) Include any additional materials that support your thesis, such as raw data, surveys. 📌P.S.: To follow this outline, you can ensure that each chapter of your PhD thesis is well-structured and contributes to the overall coherence and quality of your research. Adjust the outline according to the specific requirements of your field and the nature of your study. ◽️ Good luck, Ph.D. warriors! #phd #phdlife #phdthesis #thesisviva #academics #thesisdefense #research #researchpaper #researchdefense #academicwriting #ai #machinelearning #deeplearning #genai #ann #publication #nlp #Research_Tools #Tools #Research #ResearchTools @Recommender_Systems

𝗛𝗼𝘄 𝘁𝗼 𝗢𝘂𝘁𝗹𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗣𝗵.𝗗.𝗧𝗵𝗲𝘀𝗶𝘀 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀: 𝗔 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀' 𝗚𝘂𝗶𝗱𝗲! Good luck, Ph.D. warriors! #phd #phdlife #phdthesis #thesisviva #academics #thesisdefense #research #researchpaper #researchdefense #academicwriting #ai #machinelearning #deeplearning #genai #ann #publication #nlp #Research_Tools #Tools #Research #ResearchTools @Recommender_Systems #phd #phdlife #phdthesis #thesisviva #academics #thesisdefense #research #researchpaper #researchdefense #academicwriting #ai #machinelearning #deeplearning #genai #ann #publication #nlp

𝐋𝐢𝐬𝐭 𝐨𝐟 𝗧𝗼𝗽 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐒𝐮𝐜𝐜𝐞𝐬𝐬! Research often requires a diverse set of tools to navigate different stages of the process. The attached figure provides a list of some popular tools used for various research activities, including: • Literature Review • Reference Management Software • Writing Tools for Documentation • Data Collection and Management • Data Analysis and Visualization • Experimentation and Simulation • Research Visualization Tools • Machine Learning and Data Mining • Statistical Analysis • Collaboration and Communication ✅ Good luck with your research, PhD warriors! #phd #phdlife #phdthesis #thesisviva #academics #thesisdefense #research #researchpaper #researchdefense #academicwriting #ai #machinelearning #deeplearning #genai #ann #publication #nlp #Research_Tools #Tools #Research #ResearchTools @Recommender_Systems

🔅 Data Science Summarized Statistics = Maths Statistics + Python = Data Analytics Statistics + Python + Model = Machine Learning Statistics + Python + Model + Domain Knowledge = Data Science #Statistics #Maths #Python #Data #Domain #Knowledge #DomainKnowledge #DataScience #DS #Data_Science #Summarized @Recommender_Systems

🔅 آموزش ورد میکروسافت برای نگارش پایان نامه ◽️ تایپ با بلندگو در ورد ◽️ ترسیم دقیق شکل‌ها در اسناد تهیه شده توسط ورد ◽️ نوشتن علائم نگارشی در Word ◽️ یک سایت رایگان برای تبدیل فایل pdf فارسی به ورد #تز #ورد #نگارش #میکروسافت #پایان_نامه #Word #Type #Research_Tools #Tools #Research #ResearchTools #Microsoft #Writing #Thesis #Video #Tutorial #Tutorials @Recommender_Systems

🔅مقاله نویسی با استفاده از هوش مصنوعی ◽️ جستجو مقالات ترند ◽️ موتور جستجو مقالات ◽️ پاسخ به سوالات بر اساس مقالات ◽️ دستیار هوش مصنوعی مقاله نویسی ◽️ ابزارهای هوش مصنوعی در میکروسافت ورد ◽️ مصورسازی فضای پژوهشی و مقالات حوزه های مرتبط ◽️ پاسخ به سوالات پژوهشی با ارجاع به مقالات علمی ◽️ دستیار مقاله خوانی و تحلیل علمی ◽️ جستجو، خلاصه سازی و ترجمه سریع مقالات ◽️ ابزار نوشتاری پژوهشی ◽️ دسته بندی و مدیریت هوشمندانه مقالات ✅ لیست ابزارهای دانلود رایگان مقاله و کتاب #مقاله_نویسی #مقاله #ابزار_پژوهش #ابزار_جستجو #ربات #هوش_مصنوعی #ربات_تلگرامی #هوش_مصنوعی #هوش #پژوهش #ابزار #ربات #تلگرام #سایت #Tools #Research #Research_Tools #ResearchTools #AI #AI_Tools #Best #BestTools #Best_Tools #Bot #Download #Bot #Telegram #Journal #Paper #Free #Search @Recommender_Systems

2024 Spatial and Temporal User Interest Representations for Sequential Recommendation https://ieeexplore.ieee.org/abstract/document/10506913/keywords#keywords Abstract: In recent years, recommendation systems have become increasingly prevalent in various fields, facilitating quick access to the information users need. As a result, many models have been proposed to model user interests, leading to more accurate recommendation lists, superior user experience, and business value. However, characterizing the dynamically changing interests of users is a challenging task. User interests shift over time while maintaining some long-term interests, and at each time, users’ interests are diverse. To investigate the benefits of multidimensional interests for users, this article proposes to characterize user preferences based on their spatiotemporal interests. Utilizing temporal and spatial information is critical for improving recommendation accuracy. To achieve this, we present a novel approach called multi long short-term interest (MLSI) user representation for recommendation. This method extracts long-term and short-term interests of users from their behavioural sequences using decoupled self-supervised learning with different optimizers. Self-attention is then employed to capture the diverse interests of users through their behavioral sequences. Final, long-term and short-term interests, as well as diversified interests, are aggregated to represent user interests. Extensive experiments on real-world datasets show that MLSI not only outperforms state-of-the-art methods but also more effectively characterizes user interests, reflecting an improvement ranging from 5% to 20% across various metrics on multiple datasets. ◽️IEEE Keywords: Vectors , Recommender systems , Sports , Feature extraction , Videos , Task analysis , Spatiotemporal phenomena ◽️ Author Keywords: Long-short-term interest , multi-interest , recommendation system , sequence model #Spatial #Temporal #Sequence #Model #SequenceModel #Multi #Interest #MultiInterest #Multi_Interest #MLSI #Vector #Sports #User_Interest #UserInterest @Recommender_Systems

🔅 یک ابزار مناسب برای بهینه‌سازی پارامترها در یادگیری ماشین و شبکه‌های عصبی ◽️ بهینه‌سازی هایپر پارامترها ◽️ بهبود عملکرد مدل‌های پیش‌بینی ◽️ امکان جستجو در فضای پارامترهای پیچیده Optuna - A hyper parameter optimization framework #ML #MachineLearning #Machine_Learning #NN #Tools #Optimization #Hyper #parameter #Framework @Recommender_Systems

2024 Diarec Dynamic Intention Aware Recommendation with Attention Based Context Aware Item Attributes Modeling Abstract : Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has in- creased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other state- of-the-art methods. Keywords: Unified recommender system, User intention, Context awareness, Attention mechanism, Collaborative projection, Item attribute relation 👥 Hadise Vaghari , .... #Diarec #Dynamic #Intention #IntentionAware #Attention #Context #ContextAware #Item #Attributes #Modeling #UserIntention #Collaborative #Context_Aware @Recommender_Systems