Recommender Systems: Personalized Recommendations for Users

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Related lectures (32)

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Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Algorithm Design: Divide and Conquer

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Solving Parity Games in Practice

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Big Data Analytics: Trends and Solutions

Explores the impact of big data, covering economic value, latency-sensitive and throughput-bound applications, graph analytics, and challenges in utilizing flash storage.

Recommender Systems: Basics and Techniques

Covers collaborative filtering and content-based methods for recommender systems, addressing cold start problems and making predictions.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory and algorithms optimization, comparing minimax, alpha-beta pruning, and Monte-Carlo tree search.

Feedback Equilibrium Seeking: Robust Control for Cyber-Physical Systems

Explores Feedback Equilibrium Seeking for robust control in cyber-physical systems, emphasizing reliability, scalability, and performance.

Variance Reduction: Strategies and Applications

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.