differences among the different alternative combination of fuzzy set theoretic similarity measures and aggregation methods in their recommendation accuracy. The paper has the following main contributions Compared to using crisp set theory, the paper shows using fuzzy set theory slightly improves precision without loss of recall for content-based movies recommendation application 2. The paper provides a representation framework for features of items and users feedback using fuzzy sets as well as new algorithms for content-based item recommender systems 3. The paper presents a practical and detailed description of how to apply fuzzy set theory in a new domain as well how to conduct extensive experimental study to validate the theory of the fuzzy set theoretic aggregation methods and the fuzzy set theoretic similarity measures ombination of 4. The paper provides a guideline for recommender systems designers that will help them to choose a The remainder of the paper is organized as follows. Section 2 presents a review of related literature. Section 3 presents the representation method, inference methods, similarity measures and algorithms. Section 4 describes the dataset, evaluation settings, and evaluation metrics. Section 5 presents the results of the evaluation followed by the discussion in Section 6. Finally, our conclusions and future research directions are presented in Section 7. 2. Related literature 2./. Recommender systems There are various classifications of recommendation methods based on the sources of data and how these data are used for recommendation, Burke [8 has classified recommendation methods into: collaborative, content-based, demographic, utility-based, and knowledge-based. There are also various variants of hybrid methods that combine these methods. These hybrid methods are discussed in detail in [8]. Moreover, a recent survey of state-of-the-art recommender systems along with suggestions for improvements is found in [1] The two most widely used methods of recommendation are content-based and collaborative filtering(CF). In collab- orative filtering, an item is recommended to a user based on other similar users actions like interests, preferences and ratings[24]. Because of the availability of ratings data, CF is the most fully explored and several numbers of studies are eported Deshpande and Karypis [12] developed item-based Top-N recommendation algorithms that are collaborative ype and faster than traditional user-user collaborative algorithms with comparable recommendation hit-rate. More- over, results of evaluation of these CF algorithms for recommender systems using the MovieLens dataset are reported in terms of precision and Fl-measure in [21] In content-based recommendation, an item is recommended to a user mainly based on the characteristics of the em and the user's past actions like purchases, queries, and ratings. Moreover, in content-based recommendation, standard machine learning techniques such as clustering, Bayesian networks and induction learning are applied in forming attribute-based models [8]. Alspector et al. [2] used a set of seven movie features--category, MAAP rating, academy award, origin, length, director and Maltin rating, in addition to the rating. They showed that the pure CF method produces significantly better results(in terms of correlation measure between predicted and actual rating)than the ones obtained with the content -based method Basu et al. [6] applied inductive learning approaches that use Ripper for recommendation of movies. They showed that content-based approaches result in loss of precision with modest increase in recall; collaborative approaches improve precision with modest loss of recall; and hybrid approaches increase both precision and recall. Weng and George [27] have also reported similar result for precision. These studies indicated that the mere introduction of movie features alone does not improve precision. The present study attempts to show that a proper introduction of movie features does improve precision without loss of recall. 2.2. Fuzzy modeling Fuzzy set theory offers a rich spectrum of methods for the management of non-stochastic uncertainty. It is well suited to handle imprecise information, the un-sharpness of classes of objects or situations, and the gradualness of preference profiles [31]78 A. Zenebe, A.F. Norcio / Fuzzy Sets and Systems 160 (2009) 76–94 differences among the different alternative combination of fuzzy set theoretic similarity measures and aggregation methods in their recommendation accuracy. The paper has the following main contributions: 1. Compared to using crisp set theory, the paper shows using fuzzy set theory slightly improves precision without loss of recall for content-based movies recommendation application. 2. The paper provides a representation framework for features of items and users feedback using fuzzy sets as well as new algorithms for content-based item recommender systems. 3. The paper presents a practical and detailed description of how to apply fuzzy set theory in a new domain as well as how to conduct extensive experimental study to validate the theory. 4. The paper provides a guideline for recommender systems designers that will help them to choose a combination of one of the fuzzy set theoretic aggregation methods and the fuzzy set theoretic similarity measures. The remainder of the paper is organized as follows. Section 2 presents a review of related literature. Section 3 presents the representation method, inference methods, similarity measures and algorithms. Section 4 describes the dataset, evaluation settings, and evaluation metrics. Section 5 presents the results of the evaluation followed by the discussion in Section 6. Finally, our conclusions and future research directions are presented in Section 7. 2. Related literature 2.1. Recommender systems There are various classifications of recommendation methods. Based on the sources of data and how these data are used for recommendation, Burke [8] has classified recommendation methods into: collaborative, content-based, demographic, utility-based, and knowledge-based. There are also various variants of hybrid methods that combine these methods. These hybrid methods are discussed in detail in [8]. Moreover, a recent survey of state-of-the-art recommender systems along with suggestions for improvements is found in [1]. The two most widely used methods of recommendation are content-based and collaborative filtering (CF). In collab￾orative filtering, an item is recommended to a user based on other similar users’ actions like interests, preferences and ratings [24]. Because of the availability of ratings data, CF is the most fully explored and several numbers of studies are reported. Deshpande and Karypis [12] developed item-based Top-N recommendation algorithms that are collaborative type and faster than traditional user–user collaborative algorithms with comparable recommendation hit-rate. More￾over, results of evaluation of these CF algorithms for recommender systems using the MovieLens dataset are reported in terms of precision and F1-measure in [21]. In content-based recommendation, an item is recommended to a user mainly based on the characteristics of the item and the user’s past actions like purchases, queries, and ratings. Moreover, in content-based recommendation, standard machine learning techniques such as clustering, Bayesian networks and induction learning are applied in forming attribute-based models [8]. Alspector et al. [2] used a set of seven movie features—category, MAAP rating, academy award, origin, length, director and Maltin rating, in addition to the rating. They showed that the pure CF method produces significantly better results (in terms of correlation measure between predicted and actual rating) than the ones obtained with the content-based method. Basu et al. [6] applied inductive learning approaches that use Ripper for recommendation of movies. They showed that content-based approaches result in loss of precision with modest increase in recall; collaborative approaches improve precision with modest loss of recall; and hybrid approaches increase both precision and recall. Weng and George [27] have also reported similar result for precision. These studies indicated that the mere introduction of movie features alone does not improve precision. The present study attempts to show that a proper introduction of movie features does improve precision without loss of recall. 2.2. Fuzzy modeling Fuzzy set theory offers a rich spectrum of methods for the management of non-stochastic uncertainty. It is well suited to handle imprecise information, the un-sharpness of classes of objects or situations, and the gradualness of preference profiles [31]