Simultaneous incremental matrix factorization for streaming recommender systems
Simultaneous incremental matrix factorization for streaming recommender systems
复制标题
DOI：
10.1016/j.eswa.2020.113685
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Martin Jakomin;Z. Bosnić;Tomaž Curk
中科院分区：
文献类型：
--
作者：
Martin Jakomin;Z. Bosnić;Tomaž Curk
推荐系统是一种大规模的机器学习和知识发现工具，旨在根据客户的偏好和需求向他们提供个性化的推荐。他们需要在几秒钟内处理大量不同且非常稀疏的数据。矩阵分解技术已被证明是实现推荐系统的有效和可靠的方法，而数据稀疏性问题可以通过考虑多个异构数据源来间接缓解。此外，利用数据融合可以以更高的预测精度解决问题。对于真实世界的应用，例如具有连续用户反馈的应用，增量地处理多个数据流上的推荐系统仍然是一个关键且仅部分解决的问题。提出了一种利用矩阵分解进行数据流融合的方法。我们提出的方法(SIMF)对异类和异步数据流进行建模，并实时提供预测。由于增量更新的结果，该方法成功地适应了数据概念的变化，而数据融合的应用提高了预测精度，减少了冷启动问题的影响。使用所提出的方法，我们开发了一个流算法，并展示了如何通过考虑多个数据源来大幅提高预测精度，同时可以极大地减少冷启动的负面影响。对大规模现实问题(Yelp推荐)的评估证实了这些说法，因为我们提出了一个高度可扩展的流推荐系统，该系统适应数据中的新概念，并在非常稀疏的问题域中提供准确的预测(与其他矩阵分解技术相比)。除了本文提出的推荐系统外，矩阵分解的多功能性还可以进一步允许所提出的自适应方法解决其他几个机器学习问题，如降维、聚类和分类。
Recommender systems are large-scale machine learning and knowledge discovery tools aimed at providing personalized recommendations to customers based on their preferences and needs. They need to handle large quantities of diverse and very sparse data in a matter of seconds. Matrix factorization techniques have proven to be useful and reliable for implementing recommender systems, while data sparsity problem can be indirectly alleviated by considering multiple heterogeneous data sources. Furthermore, utilization of data fusion can resolve in a higher predictive accuracy. For real-world applications, e.g., such with continuous user feedback, incrementally handling recommender systems upon multiple data streams remains a crucial and only partially solved problem. This paper presents one way of fusing multiple data streams through matrix factorization. Our proposed method (SIMF) models heterogeneous and asynchronous data streams and provides predictions in real time. As a result of incremental updating, the proposed method successfully adapts to changes in data concepts, while application of data fusion improves prediction accuracy and reduces effects of the cold-start problem. Using the proposed methodology, we have develop a streaming algorithm and show how prediction accuracy can be substantially increased by considering multiple data sources, while at the same time the negative effects of the cold-start can be greatly diminished. Evaluations on a large-scale real-life problem (Yelp recommendations) confirm these claims as we present a highly scalable streaming recommender system that adapts to new concepts in data and provides accurate predictions (compared to the other matrix factorization techniques) in a very sparse problem domain. Apart from a recommender system proposed in this work, the versatility of matrix factorization could further allow the presented methodology for adaptation to solve several other machine learning problems, such as dimensionality reduction, clustering and classification.