Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations

TL;DR

This paper introduces STS-AEL, a novel ensemble learning framework for streaming recommender systems that uses stratified, time-aware sampling and adaptive ensemble techniques to improve accuracy by addressing concept drift and long-term preferences.

Contribution

The paper proposes a new sampling and ensemble learning framework, STS-AEL, specifically designed for real-time streaming recommendations, effectively handling concept drift and long-term user preferences.

Findings

STS-AEL significantly outperforms existing streaming recommender systems.

The framework effectively balances capturing long-term preferences and adapting to concept drift.

Experimental results on real-world datasets validate the approach's superiority.

Abstract

Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommender systems cannot handle the ubiquitous data stream well. To address this issue, Streaming Recommender Systems (SRSs) have emerged in recent years, which incrementally train recommendation models on newly received data for effective real-time recommendations. Focusing on new data only benefits addressing concept drift, i.e., the changing user preferences towards items. However, it impedes capturing long-term user preferences. In addition, the commonly existing underload and overload problems should be well tackled for higher accuracy of streaming recommendations. To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations. In STS-AEL, we first devise stratified and time-aware sampling to extract representative data from both new data and historical data to address concept drift while capturing long-term user preferences. Also, incorporating the historical data benefits utilizing the idle resources in the underload scenario more effectively. After that, we propose adaptive ensemble learning to efficiently process the overloaded data in parallel with multiple individual recommendation models, and then effectively fuse the results of these models with a sequential adaptive mechanism. Extensive experiments conducted on three real-world datasets demonstrate that STS-AEL, in all the cases, significantly outperforms the state-of-the-art SRSs.