Recommendation algorithm based on improved spectral clustering and transfer learning

Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a recommendation algorithm based on improved spectral clustering and transfer learning (RAISCTL) to improve the forecasting accuracy and generalization ability of recommender system. RAISCTL firstly improves the spectral clustering by using the eigenvalue differences and orthogonal eigenvectors and realizes the automatic determination of cluster numbers. In addition, the improved spectral clustering algorithm is used to cluster the two dimensions of the users and items of the original rating matrix. Then, RAISCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RAISCTL makes rating forecasting and recommendations based on the sharing group rating matrix and transfer learning. The simulation results show that RAISCTL can effectively improve the recommendation accuracy and generalization ability compared with other 8 conventional CF approaches.

[1]  Laurent Amsaleg,et al.  Image retrieval with reciprocal and shared nearest neighbors , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[2]  Gang Chen,et al.  kNN processing with co-space distance in SoLoMo systems , 2014, Expert Syst. Appl..

[3]  Jie Tang,et al.  A multi-ATL method for transfer learning across multiple domains with arbitrarily different distribution , 2016, Knowl. Based Syst..

[4]  Svetha Venkatesh,et al.  Multiple task transfer learning with small sample sizes , 2015, Knowledge and Information Systems.

[5]  Muriel Visani,et al.  An experimental comparison of clustering methods for content-based indexing of large image databases , 2011, Pattern Analysis and Applications.

[6]  Meng Wang,et al.  Image clustering based on sparse patch alignment framework , 2014, Pattern Recognit..

[7]  Junwei Wang,et al.  Representing conditional preference by boosted regression trees for recommendation , 2016, Inf. Sci..

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

[9]  Yuan Yan Tang,et al.  High-Order Distance-Based Multiview Stochastic Learning in Image Classification , 2014, IEEE Transactions on Cybernetics.

[10]  Yi Xiong,et al.  Learning conditional preference network from noisy samples using hypothesis testing , 2013, Knowl. Based Syst..

[11]  Masaki Aono,et al.  Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs , 2015, ACM Trans. Intell. Syst. Technol..

[12]  Rafael Valencia-García,et al.  RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes , 2015, Expert Syst. Appl..

[13]  Hongxia Zhao,et al.  Mixed collaborative recommendation algorithm based on-factor analysis of user and item: Mixed collaborative recommendation algorithm based on-factor analysis of user and item , 2011 .

[14]  Peyman Kabiri,et al.  Collaborative filtering using non-negative matrix factorisation , 2016, J. Inf. Sci..

[15]  Meng Xiaofeng and Ci Xiang,et al.  Big Data Management: Concepts，Techniques and Challenges , 2013 .

[16]  Xiaohui Hu,et al.  Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering , 2015, IEEE Transactions on Services Computing.

[17]  Anderson Rocha,et al.  Manifold Learning and Spectral Clustering for Image Phylogeny Forests , 2016, IEEE Transactions on Information Forensics and Security.

[18]  Xuelong Li,et al.  When Location Meets Social Multimedia , 2015, ACM Transactions on Intelligent Systems and Technology.

[19]  Ali I. El-Desouky,et al.  Promoting the performance of vertical recommendation systems by applying new classification techniques , 2015, Knowl. Based Syst..

[20]  Roberto Saia,et al.  Semantics-aware content-based recommender systems: Design and architecture guidelines , 2017, Neurocomputing.

[21]  Mohammad Reza Mobasheri,et al.  Clustering Multispectral Images Using Spatial–Spectral Information , 2015, IEEE Geoscience and Remote Sensing Letters.

[22]  Punam Bedi,et al.  A Proposed Framework for Group-Based Multi-Criteria Recommendations , 2014, Appl. Artif. Intell..

[23]  Juntao Liu,et al.  Conditional preference in recommender systems , 2015, Expert Syst. Appl..

[24]  MengChu Zhou,et al.  An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering , 2016, IEEE Transactions on Automation Science and Engineering.

[25]  Fei Gao,et al.  Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking , 2017, IEEE Transactions on Cybernetics.

[26]  Andreas Stafylopatis,et al.  Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models , 2017, Data Mining and Knowledge Discovery.

[27]  Yang Yang,et al.  Multitask Spectral Clustering by Exploring Intertask Correlation , 2015, IEEE Transactions on Cybernetics.

[28]  Johan A. K. Suykens,et al.  Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Yi Xiong,et al.  List-wise probabilistic matrix factorization for recommendation , 2014, Inf. Sci..

[30]  Kourosh Kiani,et al.  A new method to find neighbor users that improves the performance of Collaborative Filtering , 2017, Expert Syst. Appl..

[31]  Yiyu Yao,et al.  Modeling Tag-Aware Recommendations Based on User Preferences , 2015, Int. J. Inf. Technol. Decis. Mak..

[32]  Yasunari Yoshitomi,et al.  Music recommendation hybrid system for improving recognition ability using collaborative filtering and impression words , 2013, Artificial Life and Robotics.

[33]  Judy Kay,et al.  Recommending people to people The nature of reciprocal recommenders with a case study in online dating , 2012 .

[34]  Balázs Hidasi,et al.  General factorization framework for context-aware recommendations , 2014, Data Mining and Knowledge Discovery.

[35]  Lars Schmidt-Thieme,et al.  MyMediaLite: a free recommender system library , 2011, RecSys '11.

[36]  Mária Bieliková,et al.  Personalized hybrid recommendation for group of users: Top-N multimedia recommender , 2016, Inf. Process. Manag..

[37]  Yi Xiong,et al.  Learning Conditional Preference Networks from Inconsistent Examples , 2014, IEEE Transactions on Knowledge and Data Engineering.

[38]  Seah Hock Soon,et al.  Spectral caustic rendering of a homogeneous caustic object based on wavelength clustering and eye sensitivity , 2014, The Visual Computer.

[39]  Y. Rui,et al.  Learning to Rank Using User Clicks and Visual Features for Image Retrieval , 2015, IEEE Transactions on Cybernetics.

[40]  Juan Belmonte-Beitia,et al.  Existence of travelling wave solutions for a Fisher-Kolmogorov system with biomedical applications , 2016, Commun. Nonlinear Sci. Numer. Simul..

[41]  Youwei Wang,et al.  Term frequency combined hybrid feature selection method for spam filtering , 2014, Pattern Analysis and Applications.

[42]  Qiang Yang,et al.  Transfer learning for collaborative filtering via a rating-matrix generative model , 2009, ICML '09.

[43]  Aldo Gordillo,et al.  A Hybrid Recommendation Model for Learning Object Repositories , 2017, IEEE Latin America Transactions.

[44]  Zhendong Niu,et al.  A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining , 2017, Future Gener. Comput. Syst..