With the continuous development of e-commerce, recommender systems are facing problems such as diverse data sources, complex data structure, poor recommendation diversity and cold start. The big data recommendation methods of commodity collocation can not only solve the above problems effectively, but also have the important significance of giving suggestions to consumers and helping businesses to promote sales. Firstly, through reviewing the relevant domestic and foreign literatures, the paper explains the basic conception and form of collocation recommendation methods, and analyzes their differences and advantages compared with the traditional recommendation methods. Then, the classification of collocation recommendation methods is discussed, including collocation recommendation based on commodity content, collocation recommendation based on collaborative filtering, and hybrid collocation recommendation. Finally, based on the aforementioned research and analysis, it is pointed out that the future research hotspots will focus on the collocation recommendation of multiple commodities, collocation recommendation based on multi-source heterogeneous data fusion, and collocation recommendation based on knowledge graph. In particular, applying knowledge graph to collocation recommendation field will be a very promising research work in the future.