Action-conditional Sequence Modeling for Recommendation

TL;DR

This paper extends sequence modeling for recommendation systems by incorporating user interactions with recommended items, demonstrating improved next-item prediction performance on large-scale datasets.

Contribution

It introduces RNN architectures that include recommendation actions and state-action fusion, accounting for influence of recommendations on user behavior.

Findings

Enhanced next-item prediction accuracy

Increased modeling of recommendation influence

Effective on large-scale real-world data

Abstract

In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigation by showing items. Previous works have addressed this recommendation setup through the task of predicting the next item user will interact with. In particular, Recurrent Neural Networks (RNNs) has been shown to achieve substantial improvements over collaborative filtering baselines. In this paper, we consider interactions triggered by the recommendations of deployed recommender system in addition to browsing behavior. Indeed, it is reported that in online services interactions with recommendations represent up to 30\% of total interactions. Moreover, in practice, recommender system can greatly influence user behavior by promoting specific items. In this paper, we extend the RNN modeling framework by taking into account user interaction with recommended items. We propose and evaluate RNN architectures that consist of the recommendation action module and the state-action fusion module. Using real-world large-scale datasets we demonstrate improved performance on the next item prediction task compared to the baselines.