Abstract
The paper presents a rule based implicative rating measure to calculate the ratings of users on items. The paper also presents a new model using the ruleset with the rule length of 2 and the proposed measure to suggest to users the list of items with the highest ratings. The new model is compared to the three existing models that use items (such as the popular items, the items with highest similarities, and the items with strong relationships) to make the suggestion. The experiments on the MSWeb dataset and the MovieLens dataset indicate that the proposed recommendation model has the higher performace (via the Precision - Recall and the ROC curves) than the compared models for most of the given.
Highlights
Recommendation systems [1] are used to predict the ratings of users for products; and suggest to users the products that can be preferred by those users
In [9], we proposed a recommendation model based on the important statistical implicative measures and association rules, and conducted the internal evaluation on the performance of model
RSs help to reduce the information overload because they can suggest the valuable products to users from the rating matrices
Summary
Recommendation systems (or recommender systems - RSs) [1] are used to predict the ratings of users for products; and suggest to users the products that can be preferred by those users. The association rule based approach can provide the deep explanation on recommendation to users [5] This approach uses the support and confidence measures and the maximum rule length to find the ruleset and make the suggestion. This paper proposes a new measure called as the rule based implicative rating measure to predict the users’ ratings and a new recommendation model to present to users the top N items (e.g. movies, songs, products, etc.). The model uses the binary rating matrix of as the input; the measures including the confidence measure and the support measure for mining the rules with the rule length of 2, the statistical implicative intensity for calculating the implicative value of each rule, and the proposed measure for predicting the ratings of users on items.
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