In recent years, the Internet has been dominated by content-rich platforms, employing recommendation systems to provide users with more appealing content (e.g., videos in YouTube, movies in Netflix). While traditional content recommendations are oblivious to network conditions, the paradigm of Network-Friendly Recommendations (NFR) has recently emerged, favoring content that improves network performance (e.g. cached near the user), while still being appealing to the user. However, NFR algorithms sometimes achieve their goal by shrinking the pool of content recommended to users. The undesirable side-effect is reduced content diversity, a phenomenon known as ``content/filter bubble''. This reduced diversity is problematic for both users, who are prevented from exploring a broader range of content, and content creators (e.g. YouTubers) whose content may be recommended less frequently, leading to perceived unfairness. In this paper, we first investigate - using real data and state-of-the-art NFR schemes - the extent of this phenomenon. We then formulate a ``Diverse-NFR'' optimization problem (i.e., network-friendly recommendations with - sufficient - content diversity), and through a series of transformation steps, we manage to reduce it to a linear program that can be solved fast and optimally. Our findings show that Diverse-NFR can achieve high network gains (comparable to non-diverse NFR) while maintaining diversity constraints. To our best knowledge, this is the first work that incorporates diversity issues into network-friendly recommendation algorithms.

翻译：近年来，互联网主要由内容丰富的平台主导，这些平台采用推荐系统为用户提供更具吸引力的内容（例如YouTube的视频、Netflix的电影）。传统的推荐系统通常忽略网络条件，而网络友好推荐（NFR）这一新范式最近应运而生，它倾向于推荐能够提升网络性能（例如缓存在用户附近）且仍对用户有吸引力的内容。然而，NFR算法有时通过缩小推荐给用户的内容池来实现其目标。这种不良副作用是内容多样性的降低，即所谓的“内容/过滤气泡”现象。这种多样性降低对用户和内容创作者都存在问题：用户无法探索更广泛的内容范围，而内容创作者（例如YouTuber）的内容可能被推荐得更少，导致感知上的不公平。在本文中，我们首先利用真实数据和最先进的NFR方案研究了这一现象的程度。然后，我们提出了一个“多样化NFR”优化问题（即在保证足够内容多样性的前提下实现网络友好推荐），并通过一系列转换步骤，将其简化为一个可以快速且最优求解的线性规划问题。我们的研究结果表明，多样化NFR能够在满足多样性约束的同时实现较高的网络增益（与非多样化NFR相当）。据我们所知，这是首个将多样性问题纳入网络友好推荐算法的研究。