Recommender systems can benefit from a plethora of signals influencing user behavior such as her past interactions, her social connections, as well as the similarity between different items. However, existing methods are challenged when taking all this data into account and often do not exploit all available information. This is primarily due to the fact that it is non-trivial to combine the various information as they mutually influence each other. To address this shortcoming, here, we propose a ‘Fusion Recommender’ (FuseRec), which models each of these factors separately and later combines them in an interpretable manner. We find this general framework to yield compelling results on all three investigated datasets, Epinions, Ciao, and CiaoDVD, outperforming the state-of-the-art by more than 14% for Ciao and Epinions. In addition, we provide a detailed ablation study, showing that our combined model achieves accurate results, often better than any of its components individually. Our model also provides insights on the importance of each of the factors in different datasets.

推荐系统可以从影响用户行为的大量信号中受益，例如她过去的互动，她的社交关系以及不同项目之间的相似性。但是，在考虑所有这些数据时，现有方法面临挑战，并且通常不会利用所有可用信息。这主要是由于以下事实：将各种信息相互影响是很重要的。为了解决此缺点，在这里，我们提出了“融合推荐器”（FuseRec），该模型分别对这些因素进行建模，然后以可解释的方式将它们组合在一起。我们发现，这种通用框架可在所有三个调查数据集Epinions，Ciao和CiaoDVD上产生令人信服的结果，而Ciao和Epinions的最新技术性能要比最新技术高出14％以上。此外，我们提供了详细的消融研究，表明我们的组合模型可达到准确的结果，通常比单独的任何组件都要好。我们的模型还提供了有关不同数据集中每个因素的重要性的见解。