Abstract
A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online. In an effort to overcome the data sparsity issue in recommender systems, this research incorporates a content based filtering technique with fuzzy inference system and a conformal prediction approach introducing a new framework called Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). The proposed framework is implemented to be used in the domain of movies and it provides quality recommendations to users with a confidence level and an improved accuracy. In our proposed framework, first, a Content Based Filtering (CBF) technique is applied to create a user profile by considering the history of each user. CBF is useful in the situations like: lack of demographic information and the data sparsity problems. Second, a Fuzzy based technique is incorporated to find the similarities and differences between the user profile and the movies in the dataset using a set of fuzzy rules to get a predicted rating for each movie. Third, a Conformal prediction algorithm is implemented to calculate the non-conformity measure between the predicted ratings produced by fuzzy system and the actual ratings from the dataset. A p-value (confidence measure) is computed to give a level of confidence to each recommended item and a bound is set on the confidence level called a significance level ε, according to which the movies only above the specified significance level are recommended to user. By building a confidence centric hybrid conformal recommender system using the content based filtering approach with fuzzy logic and conformal prediction algorithm, the reliability and the accuracy of the system is considerably enhanced. The experiments are evaluated on MovieLens and Movie Tweetings datasets for recommending movies to the users and they are compared with other state-of-the-art recommender systems. Finally, the results confirm that the proposed algorithms perform better than the traditional ones.
Highlights
Recommender Systems are playing an important role since 1990’s [1] by solving information overload problem and assisting users by making intelligent decisions in suggesting them items of their interest [2,3]
An experimental evaluation of our proposed Content Based Filtering (CBF) algorithm, the fuzzy algorithm and the hybrid framework Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS) is performed on real datasets by calculating their MAE, precision, recall and f-measure and the results are compared with our proposed isolated techniques as well as other recommender systems from literature using the same datasets and evaluation metrics
By applying a conformal prediction technique on the fuzzy system and forming a hybrid framework, the recommendations are provided with a set of confidence level and the recommendation set is reduced while providing the recommendation of items within the set threshold
Summary
Recommender Systems are playing an important role since 1990’s [1] by solving information overload problem and assisting users by making intelligent decisions in suggesting them items of their interest [2,3]. We have focused on the ‘genre’ of rated movies to create user profile and recommend items according to it Other features such as age, occupation and gender of the user, in one case, is not always available as the user’s feel hesitant or consider time consuming for providing such information. One of the main advantages of using conformal prediction algorithm is that it exhibits a validation and an exchangeability property e.g. for the validation property, if we set the confidence level at 80 percent, there is 80 percent chance that the item being recommended is not error prone. A hybrid recommender system which solves data sparsity problem using a content based filtering approach and predicts ratings using a fuzzy based algorithm and improves the accuracy of the system by incorporating a confidence measure with each recommended item using a novel conformal prediction method forming a Hybrid Content based Fuzzy Conformal Recommender System (HCF-CRS). Our proposed recommender system is evaluated using a real dataset from MovieLens with 1M records and Movie Tweetings dataset against standard state-of-the-art RSs to prove its better accuracy and performance
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