An Approach for Item Recommendation Using Deep Neural Network Combined with the Bayesian Personalized Ranking

This paper proposes a deep neural network model (SDAE-BPR) based on Stack Denoising Auto-Encoder and Bayesian Personalized Ranking for the problem of accurate product recommendation. First, we use the Stack Denoising Auto-Encoder (SDAE) as the input of the item’s rating data and obtain the hidden features after encoding. Second, the Bayesian personalized Ranking (BPR) method is used to learn the hidden feature vector of the corresponding item. This model can avoid the influence of the sparseness of the matrix. Therefore, this model achieves the effect of more accurate recommendations of items. Third, to reduce the cost of model training, a unique pre-training and fine-tuning strategy is proposed in the deep neural network. Finally, based on the Movielens 20M dataset, the results of the SDAE-BPR, a traditional item-based collaborative filtering model and a user-based collaborative filtering model are compared. It is shown that the SDAE-BPR has higher accuracy. This method improves the accuracy of parameter estimation and the efficiency of model training.

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

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

[3]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[4]  Hanqing Lu,et al.  Item group based pairwise preference learning for personalized ranking , 2014, SIGIR.

[5]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[6]  Emine Yilmaz,et al.  A simple and efficient sampling method for estimating AP and NDCG , 2008, SIGIR '08.

[7]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[8]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[9]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

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

[11]  Xu Yan,et al.  Friend recommendation of microblog in classification framework: Using multiple social behavior features , 2014, 2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014).

[12]  Fernando De la Torre,et al.  Facing Imbalanced Data--Recommendations for the Use of Performance Metrics , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[13]  Dong Yu,et al.  Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features , 2016, KDD.

[14]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[15]  Zhen Chen,et al.  TST: Threshold based similarity transitivity method in collaborative filtering with cloud computing , 2013 .

[16]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[17]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[18]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[19]  Zhihua Zhang,et al.  Generalized Latent Factor Models for Social Network Analysis , 2011, IJCAI.

[20]  Elena Smirnova,et al.  Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation , 2016, RecSys.

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

[22]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[23]  Shao-Yuan Li,et al.  BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network , 2017, CIKM.

[24]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

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

[27]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.