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

Yıl 2019, Cilt 5, Sayı 2, 214 - 232, 19.12.2019
https://doi.org/10.28979/comufbed.597093

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.

Kaynakça

  • Attarde D.V., Singh M., 2017. Survey on Recommendation System Using Data Mining and Clustering Techniques. International Journal for Research in Engineering Application and Management (IJREAM), 3(9). ISSN : 2454-9150.
  • Bobadilla J., Ortega F., Hernando A., Gutiérrez A., 2013. Recommender Systems Survey. Knowledge-Based Systems, 46: 109–132.
  • Burke R., 2002. Hybrid recommender systems: Survey and experiments. User Model User Adapt Interact, 12: 331–370.
  • Cami B. R., Hassanpour H., Mashayekhi H., 2017. A Content-Based Movie Recommender System Based on Temporal User Preferences. Third Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS). DOI: 10.1109/ICSPIS.2017.8311601.
  • Campos L.M, Fernández-Luna J.M., Huete J.F., Rueda-Morales M.A., 2010. Combining Content-Based and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks. International Journal of Approximate Reasoning, 51: 785–799.
  • Chen R., Hua Q., Chang Y.S., Wang B., Zhang L., Kong X.A, 2018. Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based On Social Networks. IEE Access. DOI: 10.1109/ACCESS.2018.2877208.
  • Christakou C., Stafylopatis A., 2005. Hybrid Movie Recommender System Based on Neural Networks. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA’05). DOI: 10.1109/ISDA.2005.9.
  • David D.L., Bouldin D.W., 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1(2): 224–227. DOI:10.1109/TPAMI.1979.4766909
  • Draisma J., Horobeţ E., Ottaviani G., Sturmfels B., Thomas R., 2014. The Euclidean Distance Degree. arXiv:1309.0049.
  • Gupta U., Patil N., 2015. Recommender System Based On Hierarchical Clustering Algorithm Chameleon. IEEE International Advance Computing Conference (IACC). DOI: 10.1109/IADCC.2015.7154856.
  • Harper M.F., Konstan J.A., 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4).
  • Haruna K., Ismail M. A., Damiasih D., Sutopo J., 2017. A Collaborative Approach for Research Paper. PloS One, 12(10): 1-17.
  • Karimi, M., Jannach, D. & Jugovac, M.(2018). News recommender systems – Survey and roads ahead Information Processing & Management, 54(6): 1203-1227.
  • Koohi H., Kiani K., 2017. A New Method To Find Neighbor Users That Improves The Performance Of Collaborative Filtering. Expert Systems with Applications: An International Journal, 83(C): 30-39.
  • Kumar M., Yadav D.K., Singh A., Gupta V.K., 2015. A Movie Recommender System: MOVREC. International Journal of Computer Applications, 124(3).
  • Lekakos G., Caravelas, P., 2008. A Hybrid Approach for Movie Recommendation. Multimed Tools Appl, 36: 55–70.
  • Levandowsky M., David W., 1971. Distance Between Sets. Nature, 234(5): 34–35.
  • Mahadevan A., Arock M., 2016. A Study and Analysis of Collaborative Filtering Algorithms for Recommender Systems. International Journal of Circuit Theory and Applications, 9(27): 127-136.
  • Mierswa I., Klinkenberg R., 2019. Rapidminer Studio 9.1: Data Science, Machine Learning, Predictive Analytics.
  • Pazzani M.J., 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13: 393–408.
  • Pazzani M.J., Billsus D., 2007. Content-Based Recommendation Systems. In: Brusilovsky P., Kobsa A., Nejdl W. Eds. The Adaptive Web. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 4321.
  • Pearson K., 1895. Notes on Regression and Inheritance in the Case of Two Parents. Proceedings of the Royal Society of London, 58: 240–242.
  • Pelleg D., Moore A., 2000. X-Means: Extending K-Means with Efficient Estimation of The Number Of Clusters. In Proceedings of the 17th International Conf. on Machine Learning, 727 – 734.
  • Portugal I., Alencar P., Cowan D., 2018. The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review. Expert Systems with Applications, 97: 205 – 227.
  • Resnick P., Iacovou N., Suchak M., Bergstrom P., Riedl J., 1994. Grouplens: An Open Architecture for Collaborative Filtering Of Netnews. In Proceedings of ACM Conference on Computer Supported Cooperative Work. DOI: 10.1145/192844.192905.
  • Rombouts J., Verhoef T., (Date of access: July 2019). A Simple Hybrid Movie Recommender System. http://www.fon.hum.uva.nl/tessa/Verhoef/Past_projects_files/Eind_Rombouts_Verhoef.pdf.
  • Salton G., Wong A., Yang C.S., 1975. A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11).
  • Sridevi M., Rao R.R., Rao M.V., 2016. A survey on recommender system. International Journal of Computer Science and Information mSecurity (IJCSIS), 14(5).
  • Schwarz G.E., 1978. Estimating the Dimension of a Model. Annals of Statistics, 6(2): 461–464.
  • Tüysüzoğlu G., Işık Z., 2018. Hybrid Movie Recommendation System Using Graph-Based Approach. International Journal of Computing Academic Research (IJCAR), 7(2): 29-37.
  • Virk H.K., Singh M., Singh A., 2015. Analysis and Design of Hybrid Online Movie Recommender System. IJIET 5(2).
  • Vít N., 2018. Implementation Notes for The Soft Cosine Measure. In Proceedings The 27th ACM International Conference On Information And Knowledge Management, 1639–1642.
  • Xiao T., Shen H., 2019. Neural Variational Matrix Factorization with Side Information for Collaborative Filtering. In: Yang Q., Zhou Z.H., Gong Z., Zhang M.L., Huang S.J., Eds. Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science. Springer, Cham.
  • Zahra S., Ghazanfar M.A., Khalid A., Azam M.A., Naeem U., Bennett A.P., 2015. Novel Centroid Selection Approaches for K-Means Clustering Based Recommender Systems. Information Sciences, 320(1): 156-189.

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

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.

Kaynakça

  • Attarde D.V., Singh M., 2017. Survey on Recommendation System Using Data Mining and Clustering Techniques. International Journal for Research in Engineering Application and Management (IJREAM), 3(9). ISSN : 2454-9150.
  • Bobadilla J., Ortega F., Hernando A., Gutiérrez A., 2013. Recommender Systems Survey. Knowledge-Based Systems, 46: 109–132.
  • Burke R., 2002. Hybrid recommender systems: Survey and experiments. User Model User Adapt Interact, 12: 331–370.
  • Cami B. R., Hassanpour H., Mashayekhi H., 2017. A Content-Based Movie Recommender System Based on Temporal User Preferences. Third Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS). DOI: 10.1109/ICSPIS.2017.8311601.
  • Campos L.M, Fernández-Luna J.M., Huete J.F., Rueda-Morales M.A., 2010. Combining Content-Based and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks. International Journal of Approximate Reasoning, 51: 785–799.
  • Chen R., Hua Q., Chang Y.S., Wang B., Zhang L., Kong X.A, 2018. Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based On Social Networks. IEE Access. DOI: 10.1109/ACCESS.2018.2877208.
  • Christakou C., Stafylopatis A., 2005. Hybrid Movie Recommender System Based on Neural Networks. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA’05). DOI: 10.1109/ISDA.2005.9.
  • David D.L., Bouldin D.W., 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1(2): 224–227. DOI:10.1109/TPAMI.1979.4766909
  • Draisma J., Horobeţ E., Ottaviani G., Sturmfels B., Thomas R., 2014. The Euclidean Distance Degree. arXiv:1309.0049.
  • Gupta U., Patil N., 2015. Recommender System Based On Hierarchical Clustering Algorithm Chameleon. IEEE International Advance Computing Conference (IACC). DOI: 10.1109/IADCC.2015.7154856.
  • Harper M.F., Konstan J.A., 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4).
  • Haruna K., Ismail M. A., Damiasih D., Sutopo J., 2017. A Collaborative Approach for Research Paper. PloS One, 12(10): 1-17.
  • Karimi, M., Jannach, D. & Jugovac, M.(2018). News recommender systems – Survey and roads ahead Information Processing & Management, 54(6): 1203-1227.
  • Koohi H., Kiani K., 2017. A New Method To Find Neighbor Users That Improves The Performance Of Collaborative Filtering. Expert Systems with Applications: An International Journal, 83(C): 30-39.
  • Kumar M., Yadav D.K., Singh A., Gupta V.K., 2015. A Movie Recommender System: MOVREC. International Journal of Computer Applications, 124(3).
  • Lekakos G., Caravelas, P., 2008. A Hybrid Approach for Movie Recommendation. Multimed Tools Appl, 36: 55–70.
  • Levandowsky M., David W., 1971. Distance Between Sets. Nature, 234(5): 34–35.
  • Mahadevan A., Arock M., 2016. A Study and Analysis of Collaborative Filtering Algorithms for Recommender Systems. International Journal of Circuit Theory and Applications, 9(27): 127-136.
  • Mierswa I., Klinkenberg R., 2019. Rapidminer Studio 9.1: Data Science, Machine Learning, Predictive Analytics.
  • Pazzani M.J., 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13: 393–408.
  • Pazzani M.J., Billsus D., 2007. Content-Based Recommendation Systems. In: Brusilovsky P., Kobsa A., Nejdl W. Eds. The Adaptive Web. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 4321.
  • Pearson K., 1895. Notes on Regression and Inheritance in the Case of Two Parents. Proceedings of the Royal Society of London, 58: 240–242.
  • Pelleg D., Moore A., 2000. X-Means: Extending K-Means with Efficient Estimation of The Number Of Clusters. In Proceedings of the 17th International Conf. on Machine Learning, 727 – 734.
  • Portugal I., Alencar P., Cowan D., 2018. The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review. Expert Systems with Applications, 97: 205 – 227.
  • Resnick P., Iacovou N., Suchak M., Bergstrom P., Riedl J., 1994. Grouplens: An Open Architecture for Collaborative Filtering Of Netnews. In Proceedings of ACM Conference on Computer Supported Cooperative Work. DOI: 10.1145/192844.192905.
  • Rombouts J., Verhoef T., (Date of access: July 2019). A Simple Hybrid Movie Recommender System. http://www.fon.hum.uva.nl/tessa/Verhoef/Past_projects_files/Eind_Rombouts_Verhoef.pdf.
  • Salton G., Wong A., Yang C.S., 1975. A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11).
  • Sridevi M., Rao R.R., Rao M.V., 2016. A survey on recommender system. International Journal of Computer Science and Information mSecurity (IJCSIS), 14(5).
  • Schwarz G.E., 1978. Estimating the Dimension of a Model. Annals of Statistics, 6(2): 461–464.
  • Tüysüzoğlu G., Işık Z., 2018. Hybrid Movie Recommendation System Using Graph-Based Approach. International Journal of Computing Academic Research (IJCAR), 7(2): 29-37.
  • Virk H.K., Singh M., Singh A., 2015. Analysis and Design of Hybrid Online Movie Recommender System. IJIET 5(2).
  • Vít N., 2018. Implementation Notes for The Soft Cosine Measure. In Proceedings The 27th ACM International Conference On Information And Knowledge Management, 1639–1642.
  • Xiao T., Shen H., 2019. Neural Variational Matrix Factorization with Side Information for Collaborative Filtering. In: Yang Q., Zhou Z.H., Gong Z., Zhang M.L., Huang S.J., Eds. Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science. Springer, Cham.
  • Zahra S., Ghazanfar M.A., Khalid A., Azam M.A., Naeem U., Bennett A.P., 2015. Novel Centroid Selection Approaches for K-Means Clustering Based Recommender Systems. Information Sciences, 320(1): 156-189.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Sait Can YÜCEBAŞ (Sorumlu Yazar)
Çanakkale Onsekiz Mart Üniversitesi
0000-0002-1030-3545
Türkiye

Yayımlanma Tarihi 19 Aralık 2019
Yayınlandığı Sayı Yıl 2019, Cilt 5, Sayı 2

Kaynak Göster

Bibtex @araştırma makalesi { comufbed597093, journal = {Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi}, issn = {}, eissn = {2459-1580}, address = {}, publisher = {Çanakkale Onsekiz Mart Üniversitesi}, year = {2019}, volume = {5}, pages = {214 - 232}, doi = {10.28979/comufbed.597093}, title = {MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks}, key = {cite}, author = {Yücebaş, Sait Can} }
APA Yücebaş, S. C. (2019). MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks . Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi , 5 (2) , 214-232 . DOI: 10.28979/comufbed.597093
MLA Yücebaş, S. C. "MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks" . Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 (2019 ): 214-232 <https://dergipark.org.tr/tr/pub/comufbed/issue/50563/597093>
Chicago Yücebaş, S. C. "MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks". Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 (2019 ): 214-232
RIS TY - JOUR T1 - MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks AU - Sait Can Yücebaş Y1 - 2019 PY - 2019 N1 - doi: 10.28979/comufbed.597093 DO - 10.28979/comufbed.597093 T2 - Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi JF - Journal JO - JOR SP - 214 EP - 232 VL - 5 IS - 2 SN - -2459-1580 M3 - doi: 10.28979/comufbed.597093 UR - https://doi.org/10.28979/comufbed.597093 Y2 - 2019 ER -
EndNote %0 Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks %A Sait Can Yücebaş %T MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks %D 2019 %J Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi %P -2459-1580 %V 5 %N 2 %R doi: 10.28979/comufbed.597093 %U 10.28979/comufbed.597093
ISNAD Yücebaş, Sait Can . "MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks". Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 / 2 (Aralık 2019): 214-232 . https://doi.org/10.28979/comufbed.597093
AMA Yücebaş S. C. MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019; 5(2): 214-232.
Vancouver Yücebaş S. C. MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2019; 5(2): 214-232.
IEEE S. C. Yücebaş , "MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks", Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 5, sayı. 2, ss. 214-232, Ara. 2019, doi:10.28979/comufbed.597093

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