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.

推荐系统是大规模的机器学习和知识发现工具，旨在根据客户的喜好和需求为其提供个性化推荐。他们需要在几秒钟内处理大量多样且稀疏的数据。事实证明，矩阵分解技术对于实施推荐系统是有用且可靠的，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，利用数据融合可以提高预测精度。对于现实世界的应用程序（例如具有连续用户反馈的应用程序），在多个数据流上逐步处理推荐系统仍然是至关重要的且只能部分解决的问题。本文提出了一种通过矩阵分解融合多个数据流的方法。我们提出的方法（SIMF）对异构和异步数据流进行建模，并提供实时预测。作为增量更新的结果，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并减少了冷启动问题的影响。使用提出的方法，我们开发了一种流算法，并展示了如何通过考虑多个数据源来显着提高预测精度，同时可以大大减少冷启动的负面影响。对大规模现实生活问题的评估（Yelp建议）证实了这些主张，因为我们提供了高度可扩展的流推荐系统，该系统可适应数据中的新概念并以非常稀疏的方式提供准确的预测（与其他矩阵分解技术相比）问题域。除了在这项工作中提出的推荐系统之外，矩阵分解的多功能性还可以使所提出的适应方法能够解决其他一些机器学习问题，例如降维，聚类和分类。