DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks

TL;DR

DivNet is a novel recommendation network that models complex interactions among items to improve diversity and relevance in sequential recommendations, outperforming existing methods in both offline and online tests.

Contribution

This paper introduces DivNet, a new diversity-aware self-correcting sequential recommendation network that captures complex item interactions to enhance recommendation diversity and relevance.

Findings

DivNet outperforms baseline models in offline evaluations.

DivNet improves diversity and relevance in recommendations.

Experiments confirm DivNet's effectiveness in online settings.

Abstract

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.