Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity

Understanding user behavior and leveraging this information in recommendation systems pose challenges for websites lacking a login system or with limited logged-in users. This study introduces the Prod2Vec-Var recommendation system, a modified version of a session-based recommendation algorithm aimed at enhancing the performance of product recommendation systems with a cold-start extension. The proposed model builds upon the original prod2vec algorithm, incorporating an additional step to improve the diversity of product recommendations. The project entails a well-designed data pipeline, effectively processing user actions to align them with the model, and implementing unique functions that expand the range of products capturing users' attention. Moreover, a straightforward yet effective cold-start model is developed to address newly added products that have not been viewed by users. The outcome of our project, namely product suggestions, is presented to users of cimri.com, one of iLab's affiliated companies, which attracts millions of daily visits, thereby enabling seamless access to desired products. Experimental results demonstrated the superior performance of our model compared to the other two different strategies in running recommendation on popular products, as evidenced by favorable R@1, R@5, R@10, and R@15 metrics. Concerning less popular products, we observed an improvement in our model's performance as the value of K increased, ultimately achieving optimal results in terms of R@15. Additionally, our cold-start model for new products substantiated the efficacy of our methodology, yielding the highest scores across R@5, R@10, and R@15 metrics.