MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks

Abstract

The amount of data in World Wide Web is growing exponentially. Users are often lost in this vast ocean of data. In order to filter the valuable information from vast amount of data, recommendation systems are used. These systems are based on collaborative filtering, content based filtering and hybrid approaches. We combined collaborative and content-based filtering to build a hybrid movie recommendation system, MovieANN, based on neural network model. To make better recommendations in a collaborative approach, both user and movie clusters are formed. In addition to rating information, content information was also considered in the formation of the clusters. Clusters are formed according to K-Means and X-Means algorithms. Final clusters are chosen according to Davies-Bouldin Index and intra cluster distance. Homogeneity and density of the clusters are also considered. Movie and user clusters are mapped in the recommendation phase. The model is tested on a MoiveLens 1M dataset that consists of six thousand users, four thousand movies and one million ratings. Four clusters are formed to represent movie – user mappings and for each cluster, a recommendation model based on multi-layer neural network is constructed. The recommendation performance in terms of accuracy is 84.52%, 84.54% in terms of precision and 99.98% in terms of recall.

Keywords

• Recommendation Systems,Content Based Filtering,Collaborative Filtering,hybrid recommender

MovieANN: Film Öneri Sistemlerine Çok Katmanlı Yapay Sinir Ağı Kullanarak Karma Bir Yaklaşım

Abstract

İnternetteki veri miktarı gün geçtikçe katlanarak artmaktadır. Kullanıcılar bu geniş veri okyanusunda sıklıkla kaybolmaktadır. Bu yüksek miktardaki ham veriden önemli bilgiyi filtrelemek için öneri sistemleri kullanılır. Bu sistemler işbirlikçi filtrelemeye, içeriğe dayalı filtrelemeye ve hibrit yaklaşımlara dayanmaktadır. Bu çalışmada yapay sinir ağına dayalı hibrit bir film öneri sistemi olan MovieANN, işbirlikçi ve içerik tabanlı filtreleme kullanılarak gerçekleştirilmiştir. İşbirlikçi bir yaklaşımla daha iyi öneriler yapmak için hem kullanıcı hem de film kümeleri oluşturulmuştur. Kümeler oluşturulurken rating bilgisine ek olarak içerik bilgisi de dikkate alınmıştır. Kümeleme için K-Means ve X-Means algoritmaları kullanılmıştır.  Son kümeler, Davies-Bouldin Endeksi ve küme içi mesafelerine göre seçilir. Kümeler oluşturulurken homojenlik ve yoğunluk da göz önünde bulundurulmuştur. Öneri adımında film ve kullanıcı kümeleri eşleştirilir. İlgili model, altı bin kullanıcı, dört bin film ve bir milyon ratingden oluşan MoiveLens 1M veri kümesinde test edilmiştir. Film kullanıcı eşlemelerini temsil etmek için dört küme ve her küme için çok katmanlı sinir ağını temel alan bir öneri modeli oluşturulmuştur. Modelin öneri performansı doğruluk olarak % 84,52, kesinlik açısından % 84,54 ve geri çağırmada % 99,98'dir.

Keywords

• Öneri Sistemleri,İçerik Tabanlı Filtreleme,işbirlikçi Filtreleme,hibrit öneri

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