A New Users Rating-Trend Based Collaborative Denoising Auto-Encoder for Top-N Recommender Systems

To promote online businesses and sales, e-commerceindustry focuses to fulfill users’ demands by giving them top set ofrecommendations which are ranked through different ranking measures.Deep learning based auto-encoder models have further improved theperformance of recommender systems. Astate-of-the-art collaborative denoisingauto-encoder (CDAE) models user-item interactions as a corruptedversion of users rating inputs. However, this architecture stilllacks users’ ratings-trend information which is an important parameterto recommend top-N items to users. In this paper, buildingupon CDAE characteristics, we propose a novel users rating-trendbased collaborative denoising auto-encoder (UT-CDAE) whichdetermines user-item correlations by evaluating rating-trend(High or Low) of a user towards a set of items. This inclusion of auser’s rating-trend provides additional regularization flexibilitywhich helps to predict improved top-N recommendations. Thecorrectness of the suggested method is verified through different rankingevaluation metrics i.e., (mean reciprocal rank, meanaverage precision and normalized discounted gain), for various inputcorruption values, learning rates and regularization parameters.Experiments on standard ML-100K and ML-1M datasets showthat suggested model has improved performance overstate-of-the-art denoising auto-encodermodels.

[1]  Chia-Yu Lin,et al.  Hybrid Real-Time Matrix Factorization for Implicit Feedback Recommendation Systems , 2018, IEEE Access.

[2]  Kilian Q. Weinberger,et al.  Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.

[3]  Qingsheng Zhu,et al.  Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization , 2012, Knowl. Based Syst..

[4]  James She,et al.  Collaborative Variational Autoencoder for Recommender Systems , 2017, KDD.

[5]  Liang Chen,et al.  Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback , 2016, PAKDD.

[6]  Cihan Kaleli,et al.  A review on deep learning for recommender systems: challenges and remedies , 2018, Artificial Intelligence Review.

[7]  Dit-Yan Yeung,et al.  Relational Stacked Denoising Autoencoder for Tag Recommendation , 2015, AAAI.

[8]  Ke Wang,et al.  Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding , 2018, WSDM.

[9]  Florian Strub,et al.  Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs , 2015, NIPS 2015.

[10]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[11]  Yunni Xia,et al.  Applying the learning rate adaptation to the matrix factorization based collaborative filtering , 2013, Knowl. Based Syst..

[12]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[13]  Lei Yu,et al.  A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems , 2017, AAAI.

[14]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[15]  Sang-Wook Kim,et al.  Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework , 2019, IEEE Access.

[16]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[17]  Ruihui Mu,et al.  A Survey of Recommender Systems Based on Deep Learning , 2018, IEEE Access.

[18]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[19]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[20]  Yoshua Bengio,et al.  Marginalized Denoising Auto-encoders for Nonlinear Representations , 2014, ICML.

[21]  Florian Strub,et al.  Hybrid Recommender System based on Autoencoders , 2018 .

[22]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

[23]  Joseph A. Konstan,et al.  Introduction to recommender systems , 2008, SIGMOD Conference.

[24]  George Karypis,et al.  FISM: factored item similarity models for top-N recommender systems , 2013, KDD.

[25]  Scott Sanner,et al.  AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.

[26]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[27]  Vikram Pudi,et al.  Sequential Variational Autoencoders for Collaborative Filtering , 2018, WSDM.

[28]  Jianjun Cao,et al.  Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization , 2018, IEEE Access.

[29]  Ming He,et al.  Collaborative Additional Variational Autoencoder for Top-N Recommender Systems , 2019, IEEE Access.

[30]  Lina Yao,et al.  Next Item Recommendation with Self-Attentive Metric Learning , 2018 .

[31]  Martin Ester,et al.  Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.

[32]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[33]  Tiejian Luo,et al.  A Recommendation Model Based on Deep Neural Network , 2018, IEEE Access.

[34]  Chih-Jen Lin,et al.  A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems , 2015, ACM Trans. Intell. Syst. Technol..

[35]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[36]  Sheng Li,et al.  Deep Collaborative Filtering via Marginalized Denoising Auto-encoder , 2015, CIKM.

[37]  Shuang-Hong Yang,et al.  Collaborative competitive filtering: learning recommender using context of user choice , 2011, SIGIR.