Price Elasticity Based Recommender System
Recommender systems have been widely studied and successfully applied in a variety of areas to increase sales by guiding people toward items they are more likely to find interesting. The aim of this thesis is to develop a novel recommender system that suggests items to a client based on the appeal of a product discount. This can be applied to situations where recommendations are made from a list of discounted items such as campaign products selected into personalized sales promotion letters. To take into consideration that the products have cheaper price than usual during the campaign period, we propose an extension to an item based collaborative filtering algorithm, namely the price elasticity of demand known from the field of economics. We represent a client's rating about an item by estimating with a model own elasticity which measures the sensitivity of quantity demanded to the changes in the prices. The similarities of items are computed using cross elasticity which exhibits the substitutional and complementary effects among the products. Unlike traditional similarity metrics, this measure does not assume that the two items have to be purchased by the same clients. The proposed recommender system based on the price elasticity is applied to a real world supermarket transactions dataset. The performance of the system is evaluated on two campaign periods where recommendations are made from the discounted products. The experiments show that it is better to make recommendations based on only campaign product elasticities, without considering the elasticities of campaign products' substitutes. Furthermore, when including only the customers for whom we have found at least 5 recommendations, the performance is considerably better. In particular, when making 10 suggestions (less than 1% of all campaign products), we detect all campaign products that the clients indeed purchased. Using the best method, our approach achieves precision of 0.24, which is over 10 times better in comparison to the method currently used by the company where employees manually select recommendations based on the characteristics of customer segments. The supermarket chain has confirmed their interest in testing the proposed method in practice, hence it will be applied in real world to make more relevant recommendations to the customers.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Irene Teinemaa, Jaak Vilo