Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.

中文翻译：

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具有推荐和未知用户特征信息的无线缓存网络的成本最小化

在网络边缘缓存流行内容被认为是缓解网络压力的有希望的推动因素，因为全球数据流量的很大一部分被许多订户反复请求并因此产生冗余。另一方面，推荐因其重塑用户内容需求模式的能力而备受关注。在本文中，我们研究了推荐在利用未知用户的特征信息提高边缘缓存收益方面的实用性。为此，我们首先表征通用网络模型的平均系统成本，揭示其对推荐和缓存策略的依赖。然后，我们制定了面向联合缓存和推荐决策的成本最小化问题，考虑到每个内容提供商的缓存容量预算的限制，每个用户的推荐大小和推荐质量。然而，关于用户偏好的隐含信息使问题无法解决。为了解决这个问题，提出了一种通用的长短期记忆（LSTM）网络辅助预测范式，以借助用户的历史行为数据来获得用户的偏好模式。在此基础上，我们严格证明了获得联合最小化系统成本的最优推荐和缓存策略的 NP-hardness。因此，开发了一种迭代次优算法，该算法具有可证明的多项式时间复杂度和收敛保证。广泛的仿真结果验证了我们提出的支持 LSTM 的特征信息预测方法的有效性以及所设计的联合决策方法的收敛性能。此外，表明所提出的方案显着优于许多基准。