E-ticarete yönelik işbirliksel filtreleme tabanlı öneri sistemi

Recommender systems are one of the core engagement functions for e-commerce industry. In a typical recommender system, customer and product data is analysed and a prediction model is generated which evaluates products for prospective customers. In terms of business value, it helps individuals identify their interest among overwhelming variety of products. In this paper, a collaborative filtering based recommender system framework is proposed for Turkey's leading e-commerce platform hepsiburada. First of all, implicit feedback and customer-product prediction pairs are prepared from collected data. Second, a regularized singular value decomposition (SVD) based matrix factorization model is established for collaborative filtering (CF). Customers and products are represented with latent factor vectors. This model is trained with implicit feedback, as the SVD problem is solved with Alternating Least Squares (ALS). Third, predictions are gathered from CF model. Then, predictions are limited to ten-product recommendation sets. At last, recommendations are evaluated by behavioural data generated by prospective customers.