Meta-User2Vec: Recommendations Based on Embeddings for Users and Products Using Meta-data

A good recommender system can enhance sales both offline and on the Web, while lowering maintenance and labour costs. Recent research shows that hybrid recommender systems, built on memory-based and knowledge-based techniques, deliver good results. Hybrid recommender systems leverage strong predicting power both for existing users as well as for new users, making these methods useful for a wide range of applications. In this paper, we use a Natural Language Processing (NLP) embedding technique word2vec in the context of sequential sales data, incorporating meta-data from multiple sources. We extend the Meta-Prod2vec method with jointly trained embeddings for users and related meta-data. The embeddings are made on a unique dataset representing sequential sales and website behaviour data in multiple European countries. The embeddings and features are concatenated and fed to a Neural Network (NN) to model consumers’ behaviour. The proposed Meta-User2vec method, which advantages from both product and user meta-data, outperforms existing methods Meta-Prod2vec and user2vec.