Profit Estimation Error Analysis in Recommender Systems based on Association Rules

Fig. 1. Association graph for the Ertek, Demiriz and Cakmak [23] dataset, when minsup = 0.01
Fig. 1. Association graph for the Ertek, Demiriz and Cakmak [23] dataset,
when minsup = 0.01

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It is a challenge to estimate expected benefits from recommender systems based on association rule mining. This paper aims to address this challenge and presents a study of buying preferences of a sample of retail customers. It reveals a monotonic, non-linear relationship between the expected profits (as a function of information loss) and minimum support threshold levels, when considering transactions for a recommender system based on association rules. This finding is significant for recommender systems that utilize potential profits as a decision-making criterion.

Ertek, G., Chi, X., Yee, G., Yong, O. B., Choi, B.-G., “Profit Estimation Error Analysis in Recommender Systems based on Association Rules. In Proceedings of 2015 IEEE International Conference on Big Data (Big Data). (2015) 2138 – 2142.

Download > ertek_et_al_2015_profit_estimation_error.pdf