Abstract:
With the advent of online marketplaces which millions of people worldwide visit and make purchase every second, the shopping experience and competition between these platforms have been significantly changed and recommendation systems have become a more critical part of these platforms and gained popularity in the literature. One of these online marketplaces, in which the recommendation system plays a key role, is second hand platforms. In addition to general recommendation problems, these platforms have several problems which are specific to this domain such as compromising extremely unique item sets that makes the problem difficult with respect to other domains. In this study, we propose two staged model pipelines using state-of-the-art NLP techniques word2vec and paragraph2vec to address these problems with high quality personalized product recommendation in a scalable architecture. The model performance is evaluated on both offline experiments which are conducted on historical user clickstream dataset that is gathered from a popular second hand platform and A/B test on a production system. As a consequence of these experiments, the proposed model outperforms the baseline collaborative filtering-based models with respect to selected metrics, in addition, provides significant uplifts on several business metrics in the product system.
