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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\u2019 choices are usually affected by their direct and even indirect friends\u2019 preferences. This paper proposes a hybrid recommendation model BRS\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    S (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                    <jats:italic>N<\/jats:italic>\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\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    S algorithm proposed outperforms other recommendation algorithms such as BRS\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    , UserCF, and HRS\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    . The BRS\n                    <jats:sub>\n                      <jats:italic>c<\/jats:italic>\n                    <\/jats:sub>\n                    S model is also scalable and can fuse new types of data easily.\n                  <\/jats:p>","DOI":"10.1186\/s13638-020-01716-2","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T08:02:36Z","timestamp":1592294556000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["BRS cS: a hybrid recommendation model fusing multi-source heterogeneous data"],"prefix":"10.1186","volume":"2020","author":[{"given":"Zhenyan","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Enrique","family":"Armend\u00e1riz-i\u00f1igo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Arce-Urriza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"key":"1716_CR1","doi-asserted-by":"publisher","first-page":"40416","DOI":"10.1109\/ACCESS.2019.2897586","volume":"7","author":"Z. 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