How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms
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This chapter is mainly devoted to the question of estimating transition probabilities taking into account the effect of recommendations. It turned out that this is an extremely complex problem. The central result is a simple empirical assumption that allows reducing the complexity of the estimation in a way that is computationally suitable to most practical problems. The discussion of this approach gives a deeper insight into essential principles of realtime recommendation engines. Based on this assumption, we propose methods to estimate the transition probabilities and provide some first experimental results. Although the results look promising, more advanced techniques are highly desirable. Such techniques like hierarchical and factorization methods are presented in the following chapters.