Recommending Accurate and Diverse Items Using Bilateral Branch Network
This work is significant for online platforms and users, aiming to improve both recommendation accuracy and diversity, which is an incremental improvement to existing recommender systems.
This paper addresses the trade-off between accuracy and diversity in recommender systems by proposing an adaptive learning framework. The framework, based on a bilateral branch network, adaptively balances accurate and diversified recommendations, outperforming state-of-the-art baselines on three real-world datasets.
Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden user's horizons as well as to promote enterprises' sales. However, the trading-off between accuracy and diversity remains to be a big challenge, and the data and user biases have not been explored yet. In this paper, we develop an adaptive learning framework for accurate and diversified recommendation. We generalize recent proposed bi-lateral branch network in the computer vision community from image classification to item recommendation. Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network. We also capture user level diversity using a two-way adaptive metric learning backbone network in each branch. We conduct extensive experiments on three real-world datasets. Results demonstrate that our proposed approach consistently outperforms the state-of-the-art baselines.