AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

[1]  Bowen Zhou,et al.  ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs , 2015, TACL.

[2]  Jungang Lou,et al.  A novel matrix factorization model for recommendation with LOD-based semantic similarity measure , 2019, Expert Syst. Appl..

[3]  Tat-Seng Chua,et al.  Item Silk Road: Recommending Items from Information Domains to Social Users , 2017, SIGIR.

[4]  Cristian Sminchisescu,et al.  Spatio-Temporal Attention Models for Grounded Video Captioning , 2016, ACCV.

[5]  Prem Melville and Raymond J. Mooney and Ramadass Nagarajan.  Content-Boosted Collaborative Filtering , 2001 .

[6]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

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

[8]  Tat-Seng Chua,et al.  Neural Factorization Machines for Sparse Predictive Analytics , 2017, SIGIR.

[9]  Francesco Romani,et al.  Adaptive nonnegative matrix factorization and measure comparisons for recommender systems , 2019, Appl. Math. Comput..

[10]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[11]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[12]  Neil Yorke-Smith,et al.  TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings , 2015, AAAI.

[13]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[14]  Tong Zhao,et al.  Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.

[15]  Tina Eliassi-Rad,et al.  A Probabilistic Model for Using Social Networks in Personalized Item Recommendation , 2015, RecSys.

[16]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[17]  Tat-Seng Chua,et al.  Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks , 2017, IJCAI.

[18]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[19]  Xiangnan He,et al.  Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention , 2017, SIGIR.

[20]  Jiming Liu,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Social Collaborative Filtering by Trust , 2022 .

[21]  Yang Gao,et al.  Attributes coupling based matrix factorization for item recommendation , 2017, Applied Intelligence.

[22]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[23]  Lin Cai,et al.  mrMoulder: A recommendation-based adaptive parameter tuning approach for big data processing platform , 2019, Future Gener. Comput. Syst..

[24]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[25]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[26]  Lars Schmidt-Thieme,et al.  Taxonomy-driven computation of product recommendations , 2004, CIKM '04.

[27]  Marcin Woźniak,et al.  Recommendation Based on Review Texts and Social Communities: A Hybrid Model , 2019, IEEE Access.

[28]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

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

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

[31]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[32]  Burak Kantarci,et al.  Multimedia recommendation and transmission system based on cloud platform , 2017, Future Gener. Comput. Syst..

[33]  Jiebo Luo,et al.  Image Captioning with Semantic Attention , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[35]  Anuraj Mohan,et al.  A social recommender system using deep architecture and network embedding , 2018, Applied Intelligence.