Learning to Identify Complementary Products from DBpedia

Identifying the complementary relationship between products, like a cartridge to a printer, is a very useful technique to provide recommendations. These are typically purchased together or within a short time frame and thus online retailers benefit from it. Existing approaches rely heavily on transactions and therefore they suffer from: (1) the cold start problem for new products; (2) the inability to produce good results for infrequently bought products; (3) the inability to explain why two products are complementary. We propose a framework that aims at alleviating these problems by exploiting a knowledge graph (DBpedia) in addition to products’ available information such as titles, descriptions and categories rather than transactions. Our problem is modeled as a classification task on a set of product pairs. Our starting point is the semantic paths in the knowledge graph linking between product attributes, from which we model product features. Then, having a labeled set of product pairs we learn a model; and finally, we use this model to predict complementary products for an unseen set of products. Our experiments on a real world dataset from Amazon show high performance of our framework in predicting whether one product is complementary to another one.