Association-based recommender system using statistical implicative cohesion measure

The strength of the association rule-based approach compared to other approaches in building recommender systems is that it can provide the deep explanations. Besides, evaluating the quality of generated rules to obtain the better recommendations is also necessary. This can be completed by using the statistical implicative cohesion measure - a measure used for finding the rules with strong implicative relationships. The higher the cohesion value of a rule is, the better the quality of that rule is. This paper proposes a new approach based on the association rules and the cohesion measure to discover the tendencies in a data set and recommend the top items to a user. The proposed recommender system is tested on the data sets Groceries and CourseRegistration. Depending on the purpose of users, they can change the thresholds on the measure to observe the tendencies as well as to get the top recommendations.