DivNet通过自校正机制提升推荐多样性与整体相关性。
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks
- 引入自校正机制捕捉序列推荐项间的复杂交互
- 在离线与在线测试中均优于基线模型
- 适合需要平衡多样性与相关性的推荐场景
作为典型推荐系统中的最后阶段，集体推荐旨在优化整体目标（如多样性与全页相关性）对推荐结果及其布局进行最终调整。然而，实践中推荐项之间的交互动态、视觉呈现及元数据（如规格信息）往往过于复杂，难以通过专家经验或简单模型捕捉。为此，我们提出一种多样性感知的自校正序列推荐网络（DivNet），能够通过捕捉序列项间的复杂交互来评估效用，并同时实现推荐多样化。在离线与在线设置下的实验表明，DivNet相比有无集体推荐的基线模型均取得更优表现。
原文摘要 · Abstract (English)
As the last stage of a typical \textit{recommendation system}, \textit{collective recommendation} aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a \textit{\underline{div}ersity-aware self-correcting sequential recommendation \underline{net}works} (\textit{DivNet}) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that \textit{DivNet} can achieve better results compared to baselines with or without collective recommendations.
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