Many e-commerce sites use recommender systems for suggesting products that are most likely to be preferred by a particular consumer. Though recommender systems have achieved great success, they have not reached their full potential. Most current systems do not take into account the dynamic properties of the offering and in particular product price or discount which could dramatically improve the effectiveness of a recommendation and the revenue of an e-commerce site. In this talk, I will present how dynamic pricing can be incorporated into a recommender system in different real-world scenarios to improve recommendations’ effectiveness and discount optimization. In particular, I will present concrete evidence from large e-commerce sites such as eBay.
Asi Messica pursues her Ph.D. in Software and Information Systems Engineering at the Ben-Gurion University under the supervision of Prof. Lior Rokach. Asi holds a B.Sc. in Physics and Computer Science, and MSc in Physics from the Tel Aviv University, as well as an MBA in Marketing from Bar-Ilan University. Before her Ph.D. studies, she held various product and development management positions at SAP, RSA and more. Her research interests include machine learning, recommender systems, and information retrieval.