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

Yıl 2019, Cilt: 5 Sayı: 2, 214 - 232, 19.12.2019

Sait Can Yücebaş

https://doi.org/10.28979/comufbed.597093

Cited By: 5

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

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