Evolving temporal association rules in recommender system

Abstract

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 differences 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.

Television Rating Control in the Multichannel Environment Using Trend Fuzzy Knowledge Bases and Monitoring Results †

“…In (11), (13), and (15), the trend fuzzy relations are tuned for each content category. In (12) and (14), the trend fuzzy relations are tuned regardless of the genre. The results of the primary fuzzy model tuning are presented in Appendix A. Parameters of the membership functions for the input and output primary fuzzy terms are presented in Tables A1 and A2.…”

“…In neural computing, the rule mining problem is solved using the traditional "find all-then prune and select" approach [12,13]. Rule generation using the support vector machine methodology provides enhanced recommendation accuracy [14].…”

“…The weekly TV program set was refined using the methods of genetic tuning applied to the linguistic models enhanced with SVD [3,7,20]; min-max neural networks [12]; SVM [14]. The given method simplifies the process of the program set refinement by solving the primary system of fuzzy relational equations.…”

“…Candidate rule generation corresponds to the construction of a zero option of the timetable. In works [3,7,12,14,20], candidate rule generation requires solving the optimization problem with 2nZ variables for boundaries of interval rules. Application of the hybrid approach allows reducing the number of tuning parameters by solving Z optimization problems for 2T boundaries of significance measures [25][26][27].…”

“…In content-based filtering, selection requires solving the optimization problem with nZ variables. A term selection sign with the possibility of merging the similar terms, as well as the degree of the rule relevance, are subject to tuning [3,7,12,14,20]. In the case of Z solutions of the trend system of equations, the selection is reduced to maintaining the level of detail and the density of coverage.…”