Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendation, and fusing diﬀerent types of data can make recommendation 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 BRScS (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 BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, HRSc. BRScS model is also scalable and can fuse new type of data easily.