Evolving temporal association rules in recommender system

Huiyu Zhou, Kotaro Hirasawa
2017 Neural computing & applications (Print)  
This research involves implementation of genetic network programming (GNP) and ant colony optimization (ACO) to solve the sequential rule mining problem for commercial recommendations in time-related transaction databases. Excellent recommender systems should be capable of detecting the customers' preference in a proactive and efficient manner, which requires exploring customers' potential needs with an accurate and timely approach. Due to the changing nature of customers' preferences and the
more » ... fferences with the traditional find-allthen-prune approach, the interesting temporal association rules are extracted by the metaheuristics, genetic algorithms-based method of GNP. Additionally, a useful model is constructed using the obtained rules to forecast future customer needs and an ACO approach to evolve the online recommender system continuously. The methodology is experimentally evaluated in a real-world application by analysing the customer database of an online supermarket.
doi:10.1007/s00521-017-3217-z fatcat:xouwyhbllrbnjp7vdweb36ue6y