Complete Guide To Machine Learning Based Recommendation System Ppt Presentation ML CD

Slide 1: This slide introduces Complete Guide to Machine Learning Based Recommendation System.
Slide 2: This slide states Agenda of the presentation.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This slide highlights title for topics that are to be covered next in the template.
Slide 5: This slide depicts the current scenario of decreasing subscribers (churn) on online platform and its corresponding key insights.
Slide 6: This slide highlights the key issues arising from irrelevant content suggestions-user frustration, increased cognitive load, low conversion rate and wasted marketing efforts resulting in subscriber churn.
Slide 7: This slide presents alternative solutions for enhancing content recommendation system and includes options such as community voting, machine learning and social listening.
Slide 8: This slide highlights title for topics that are to be covered next in the template.
Slide 9: This slide introduces the concepts of Machine Learning on which the recommendation system is based and its key attributes.
Slide 10: This slide outlines the four essential steps in the operation of recommendation systems: collecting and storing user data, analyzing this data, and filtering information to make personalized recommendations.
Slide 11: This slide highlights the transformative impact of machine learning-driven recommendation systems, underscoring their role in personalizing user experiences, driving sales, shaping consumer behavior and maximizing transaction values.
Slide 12: This slide highlight the key relationship of recommender systems including user-product, product-product, and user-user.
Slide 13: This slide explores the critical types of data-user behavior, demographic, and product attribute-that recommender systems leverage to curate personalized suggestions.
Slide 14: This slide highlights the distinction between explicit and implicit ratings in feeding data to recommender systems, emphasizing their roles in understanding user preferences.
Slide 15: This slide highlights the main challenges faced by recommendation systems, including the difficulty of selecting right system, cold start problem, adapting to changing user preferences and navigating privacy concerns.
Slide 16: This slide outlines the step-by-step process of building a recommendation system using machine learning, from problem identification through to deployment.
Slide 17: This slide highlights title for topics that are to be covered next in the template.
Slide 18: This slide provides market overview and CAGR analysis of recommendation engine market and its corresponding key insights highlights reasons for growth.
Slide 19: This slide provides region wise market growth rate analysis of recommendation engine market and its corresponding key insights highlights reasons for growth.
Slide 20: This slide provides an overview of leading enterprises in the recommendation engine market, highlighting their core strengths and contributions to advancing personalized user experiences.
Slide 21: This slide highlights title for topics that are to be covered next in the template.
Slide 22: This slide discusses the types of recommendation system including content-based, collaborative, and hybrid recommender systems, highlighting their unique approaches to generating personalized suggestions.
Slide 23: This slide highlights title for topics that are to be covered next in the template.
Slide 24: This slide outlines the fundamentals of content-based filtering, emphasizing its focus on individual user preferences and item characteristics to tailor recommendations.
Slide 25: This slide presents the foundational elements of content-based filtering, including the utility matrix, user and item profiles, and the application of cosine distance for similarity measurement.
Slide 26: This slide outlines the methodologies and considerations in developing content-based recommender systems, focusing on user ratings and content description.
Slide 27: This slide outlines the step-by-step process of a content-based recommender system, highlighting the journey from data collection to personalized item recommendations.
Slide 28: This slide condenses the technical prerequisites for developing a content-based filtering system, highlighting the importance of machine learning insights, programming proficiencyb.
Slide 29: This slide highlights the advantages of content-based filtering systems, including their independence from other users’ data, tailored recommendations, transparency, efficiency in the early stages, and simplicity in creation.
Slide 30: This slide highlights the core challenges of content-based filtering, emphasizing limited diversity, scalability, accuracy, and the risk of user profile stagnation.
Slide 31: This slide highlights title for topics that are to be covered next in the template.
Slide 32: This slide outlines collaborative filtering, emphasizing its role in understanding and leveraging user preferences for personalized recommendations.
Slide 33: This slide describes the workflow of the collaborative filtering approach, highlighting the key steps from data structuring to the delivery of personalized recommendations.
Slide 34: This slide provides an overview of collaborative filtering methods, highlighting their fundamental approaches and objectives in improving recommendation systems.
Slide 35: This slide outlines the primary types of memory based collaborative filtering: User-User based ad Item-Item based emphasizing their core principles.
Slide 36: This slide outlines model based collaborative filtering, emphasizing Matrix Factorization and Clustering to refine recommendation accuracy and relevance.
Slide 37: This slide showcases the key benefits of collaborative filtering: Automatic learning, uncovers new interests, operates with minimal data, and continually enhances personalization.
Slide 38: This slide highlights the challenges of collaborative recommender systems, focusing on data sparsity, scalability issues, limited diversity, and the cold start problem.
Slide 39: This slide highlights title for topics that are to be covered next in the template.
Slide 40: This slide introduces Hybrid Recommender Systems, showcasing their role in integrating Content-Based and Collaborative Filtering for improving recommendations.
Slide 41: This slide outlines the seven types of hybrid recommendation systems, each leveraging different strategies to optimize and personalize recommendations.
Slide 42: This slide outlines hybrid recommender Systems' benefits, focusing on accuracy, diversity, robustness, and personalization.
Slide 43: This slide outlines the primary challenges faced by hybrid recommender systems, including issues with timeliness, data sparsity, lack of standardization, and the cold start problem.
Slide 44: This slide highlights title for topics that are to be covered next in the template.
Slide 45: This slide explains various other types of recommendation systems including Boltzmann machine, autoencoders, knowledge based systems etc highlighting their core mechanisms and benefits for personalized user interactions.
Slide 46: This slide highlights title for topics that are to be covered next in the template.
Slide 47: This slide provides a comparative analysis of various types of recommendation systems to identify most suitable option.
Slide 48: This slide highlights title for topics that are to be covered next in the template.
Slide 49: This slide outlines the steps for implementing a recommendation system, from defining its purpose to deploying the model, ensuring a structured approach to development.
Slide 50: This slide highlights title for topics that are to be covered next in the template.
Slide 51: This slide outlines the key evaluation metrics for recommendation engines, focusing on Recall, Precision, RMSE, Mean Reciprocal Rank, and MAP at k.
Slide 52: This slide highlights title for topics that are to be covered next in the template.
Slide 53: This slide offers tips for ML-based recommender engines, focusing on choosing the right approach, user journey adaptation, context-specific strategies, and deciding between custom or platform-based solution.
Slide 54: This slide highlights title for topics that are to be covered next in the template.
Slide 55: This slide depicts the positive impact of implementing ML based recommendation system on customer churn numbers and its corresponding key insights.
Slide 56: This slide highlights title for topics that are to be covered next in the template.
Slide 57: This slide offers tips for ML-based recommender engines, focusing on choosing the right approach, user journey adaptation, context-specific strategies, and deciding between custom or platform-based solution.
Slide 58: This slide highlights title for topics that are to be covered next in the template.
Slide 59: This slide showcases the future of ML based recommendation systems such as deep learning integration, temporal dynamics etc.
Slide 60: This slide contains all the icons used in this presentation.
Slide 61: This slide is titled as Additional Slides for moving forward.
Slide 62: This slide describes how hybrid recommender systems function, highlighting mixed, weighted, meta-level hybridization, and feature combination strategies for enhanced recommendations.
Slide 63: This slide shows Drug recommendation system workflow using ML.
Slide 64: This slide presents Tourism product recommendation system workflow using ML.
Slide 65: This is About Us slide to show company specifications etc.
Slide 66: This is Our Mission slide with related imagery and text.
Slide 67: This is Our Team slide with names and designation.
Slide 68: This slide depicts Venn diagram with text boxes.
Slide 69: This slide presents Roadmap with additional textboxes.
Slide 70: This slide shows Post It Notes. Post your important notes here.
Slide 71: This slide presents Bar chart with two products comparison.
Slide 72: This is a Thank You slide with address, contact numbers and email address.