Personalization Engines: Advanced Recommender Systems for Digital Commerce

By: Malekpour Alamdari, Pegah

Unlock the transformative power of recommender systems with "Personalization Engines: Advanced Recommender Systems for Digital Commerce." This comprehensive guide delves into the heart of recommendation technologies, exploring their development, methodologies, and impact across various domains such as e-commerce, media and entertainment, social media, and online advertising.

Key Features:

In-Depth Exploration: Gain a deep understanding of the foundational principles and advanced techniques that underpin modern recommender systems, including collaborative filtering, content-based filtering, and hybrid models.Cutting-Edge Technologies: Discover the latest advancements in deep learning, reinforcement learning, graph neural networks, and natural language processing, and how they are revolutionizing the field of recommender systems.Real-World Case Studies: Learn from detailed case studies that highlight practical applications and the significant impact of recommender systems on user engagement, satisfaction, and business outcomes.Ethical Considerations: Address the crucial ethical issues related to algorithmic bias, privacy, transparency, and fairness, and explore strategies to develop responsible and equitable recommender systems.Comprehensive Evaluation Techniques: Master the tools and methodologies for evaluating recommender systems, including offline and online evaluation, A/B testing, and multi-armed bandits, to ensure their effectiveness and reliability.Future Trends and Research Avenues: Stay ahead of the curve by exploring emerging trends and potential research directions that promise to shape the future of personalized digital experiences.

• Format: Paperback
• Language: English
• ISBN: B0D99SH5YS
• ISBN13: 9798332858765
• Release Date: July 2024
• Publisher: Independently Published
• Length: 154 Pages
• Weight: 0.61 lbs.
• Dimensions: 0.3" x 7.0" x 10.0"