An approximation algorithm for joint caching and recommendations in cache networks
Dimitra Tsigkari, Thrasyvoulos Spyropoulos

TL;DR
This paper introduces a novel approximation algorithm for jointly optimizing caching and recommendation strategies in networked streaming platforms, balancing streaming quality and recommendation relevance to enhance user experience.
Contribution
It formulates the first polynomial-time approximation algorithm with a constant ratio for the combined caching and recommendation optimization problem.
Findings
Algorithm achieves a constant approximation ratio.
Significant performance improvements over baseline methods.
Numerical results validate the effectiveness of the proposed approach.
Abstract
Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, the user's overall experience is a product of both the user's interest in a recommended content, i.e., the recommendation quality (RQ), and the SQ of this content. However, network decisions (like caching) that affect the SQ are usually made without considering the recommender's actions. Likewise, recommendations are chosen independently of the potential delivery quality. In this paper, we define a metric of streaming experience (MoSE) that captures the fundamental tradeoff between the SQ and RQ. We aim to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing…
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