Using Word2Vec Recommendation for Improved Purchase Prediction


Esmeli R., Bader-El-Den M., Abdullahi H.

2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, England, 19 - 24 July 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ijcnn48605.2020.9206871
  • City: Virtual, Glasgow
  • Country: England
  • Keywords: browsing behaviour, Classification, Machine Learning, Purchase behaviour prediction, Purchase Intent, Word2vec Product Recommendation
  • Van Yüzüncü Yıl University Affiliated: No

Abstract

Purchase prediction can help e-commerce planners plan their stock and personalised offers. Word2Vec is a well-known method to explore word relations in sentences for sentiment analysing by creating vector representation of words. Word2Vec models are used in many works for product recommendations. In this paper, we analyse the effect of item similarities in the sessions in purchase prediction performance. We choose the items from different position of the session, and we derive recommendations from selected items using Word2Vec model. We assess the similarities between items by analysing the number of common recommendations of selected items. We train classification algorithms after we include similarity calculations of the selected items as session features. Computational experiments show that using similarity values of the interacted items in the session improves the performance of purchase prediction in terms of F1 score.