Behavior and Score Similarity Based Algorithm for Association Rule Group Recommendation
Zhang Jia-l · 2014
Applying the association rule recommendation tool often meets the hardness of selection for optimal rule,inadequate utilization of rule information.Using easily obtained background knowledge can solve these problems.Aiming at the situation that only contains commodity's name and score information,this paper proposed a behavior and score similarity based association rule group recommendation algorithm,in which the rule with its scores is regarded as an expert,and the experts with same conclusion are grouped together and the expert weights are calculated based on both behavior similarity and score similarity.A better recommendation suggestion is reached by aggregating the recommendation opinions of the experts.The experimental example shows that the algorithm is feasible and effective.