A Scalable Re-Ranking Optimization Approach for Real-Time Network-Friendly Recommendations

Network-friendly recommendation (NFR) has emerged as a promising method to enhance network performance. However, current NFR methods incur significant computational overhead and predominantly focus on content recommendations, often neglecting the importance of content ranking, which can limit recommendation quality. This article addresses these challenges by proposing a recommendation re-ranking algorithm designed to maximize ranking-aware recommendation quality while accommodating diverse network scenarios and conditions. We formulate the problem as an integer programming (IP) problem that maximizes the mean reciprocal rank of the baseline recommendation under network constraints. To tackle the computational challenges posed by large-scale IP problems, we propose a low-complexity algorithm that enables real-time NFR by re-ranking only the candidate list instead of the entire content list. Results on the real-world data sets manifest that compared with the state-of-the-art (SOTA) NFR baseline, our method achieved a 40.3% increase in cache hit rate (CHR) and a 25.32% reduction in transmission latency. Additionally, the recommendation quality is further enhanced, with improvements of more than 3.5% in CHR and 2.62% in latency reduction scenarios. Moreover, computational time is reduced by over 97%.