Heuristic Optimization for Intelligent Recommender Systems

With the development of information technology and big data, the problem of information overload faced by users is becoming increasingly prominent, and personalized recommendation has become an indispensable part of modern information systems. Intelligent Recommender Systems (IRS) analyze users' historical behavior and item characteristics to achieve personalized information delivery, improving user experience and business value. However, traditional recommendation methods suffer from static user interests, strong data sparsity, and local optima. To address these issues, this paper proposes a novel heuristic optimization algorithm for IRS, whose core innovations include a dynamic interest coupling mechanism, an uncertainty-driven heuristic exploration mechanism, a cross-domain feature fusion mechanism, and an adaptive perturbation optimization mechanism. This paper details the algorithm's design philosophy, mathematical formula derivation, and optimization process, demonstrating the algorithm's potential and applicability in intelligent recommendation systems, and providing new ideas and theoretical foundations for future research on recommendation systems.