Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually affected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRS _cS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRS _cS algorithm proposed outperforms other recommendation algorithms such as BRS _c, UserCF, and HRS _c. The BRS _cS model is also scalable and can fuse new types of data easily.

推荐系统通常用于解决Internet上信息过载的问题。许多类型的数据都可以用于推荐，融合不同类型的数据可以使推荐更准确。大多数现有的融合推荐模型只是将来自不同数据的推荐结果组合在一起，而不是完全融合多源异构数据来提出推荐。此外，用户的选择通常受其直接甚至是间接朋友的偏好的影响。本文提出了一种混合推荐模型BRS _c S（BPR-Review-Score-Social的缩写）。它将社交数据，评分和评论完全融合在一起；使用改进的BPR模型来优化排名；并在联合表示学习框架中对其进行培训，以获得前N名建议。用户信任模型用于将社会关系引入评分和评论数据，PV-DBOW模型用于处理评论数据，而全连接神经网络用于处理评分数据。在Yelp公开数据集上的实验表明，所提出的BRS _c S算法优于其他推荐算法，例如BRS _c，UserCF和HRS _c。BRS _c S模型还具有可伸缩性，可以轻松融合新型数据。