Hybrid Movie Recommender System based on Resource Allocation

Recommender Systems are inevitable to personalize user’s experiences on the Internet. They are using different approaches to recommend the Top-K items to users according to their preferences. Nowadays recommender systems have become one of the most important parts of largescale data mining techniques. In this paper, we propose a Hybrid Movie Recommender System (HMRS) based on Resource Allocation to improve the accuracy of recommendation and solve the cold start problem for a new movie. HMRS-RA uses a self-organizing mapping neural network to clustering the users into N clusters. The users' preferences are different according to their age and gender, therefore HMRS-RA is a combination of a Content-Based Method for solving the cold start problem for a new movie and a Collaborative Filtering model besides the demographic information of users. The experimental results based on the MovieLens dataset show that the HMRS-RA increases the accuracy of recommendation compared to the state-of-art and similar works.

[1]  Rahul Katarya,et al.  Efficient music recommender system using context graph and particle swarm , 2017, Multimedia Tools and Applications.

[2]  Jenq-Neng Hwang,et al.  Solving the Sparsity Problem in Recommendations via Cross-Domain Item Embedding Based on Co-Clustering , 2019, WSDM.

[3]  Mostafa Khalaji,et al.  CUPCF: combining users preferences in collaborative filtering for better recommendation , 2019, SN Applied Sciences.

[4]  Mohamad Saraee,et al.  FARS: Fuzzy Ant based Recommender System for Web Users , 2011 .

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

[6]  Hao Liu,et al.  Design of Clothing Clustering Recommendation System on SOM Neural Network , 2018 .

[7]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[8]  Haitao Liu,et al.  An entropy-based clustering ensemble method to support resource allocation in business process management , 2015, Knowledge and Information Systems.

[9]  Donghui Wang,et al.  A content-based recommender system for computer science publications , 2018, Knowl. Based Syst..

[10]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[11]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[12]  Yongtae Woo,et al.  A Hybrid Recommender System Combining Collaborative Filtering with Neural Network , 2002, AH.

[13]  Nabil Belacel,et al.  Scalable Collaborative Filtering Based on Splitting-Merging Clustering Algorithm , 2018, ICAART.

[14]  Mahdi Jalili,et al.  Recommender systems based on collaborative filtering and resource allocation , 2014, Social Network Analysis and Mining.

[15]  Michael R. Lyu,et al.  Learning to recommend with social trust ensemble , 2009, SIGIR.

[16]  Wei Xu,et al.  Link prediction combining network structure and topic distribution in large-scale directed network , 2020, J. Organ. Comput. Electron. Commer..

[17]  Yossi Matias,et al.  Suggesting friends using the implicit social graph , 2010, KDD.

[18]  Mohammad Al Hasan,et al.  A Survey of Link Prediction in Social Networks , 2011, Social Network Data Analytics.

[19]  Deren Chen,et al.  A hybrid approach for movie recommendation via tags and ratings , 2016, Electron. Commer. Res. Appl..

[20]  Paulo J. G. Lisboa,et al.  Grocery shopping recommendations based on basket-sensitive random walk , 2009, KDD.

[21]  Peter Knees,et al.  Multimedia recommender systems , 2018, RecSys.

[22]  Hamid Hassanpour,et al.  User preferences modeling using dirichlet process mixture model for a content-based recommender system , 2019, Knowl. Based Syst..

[23]  Alireza Abdollahpouri,et al.  CNDP: Link prediction based on common neighbors degree penalization , 2020, Physica A: Statistical Mechanics and its Applications.

[24]  Fangyi Hu,et al.  Three-Segment Similarity Measure Model for Collaborative Filtering , 2018, DMBD.

[25]  Yanguang Shen,et al.  A Neural Networks-Based Clustering Collaborative Filtering Algorithm in E-Commerce Recommendation System , 2009, 2009 International Conference on Web Information Systems and Mining.

[26]  Yaohui Li,et al.  Link Prediction Based on Multi-steps Resource Allocation , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[27]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

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

[29]  Kyong Joo Oh,et al.  The collaborative filtering recommendation based on SOM cluster-indexing CBR , 2003, Expert Syst. Appl..

[30]  Zhendong Niu,et al.  A hybrid recommender system for e-learning based on context awareness and sequential pattern mining , 2017, Soft Computing.

[31]  Fei Wu,et al.  Modeling recommender systems via weighted bipartite network , 2017, Concurr. Comput. Pract. Exp..

[32]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[33]  Feng Xia,et al.  TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item Recommendation , 2017, IEEE Systems Journal.

[34]  Dong Guo,et al.  SOM Clustering Collaborative Filtering Algorithm Based on Singular Value Decomposition , 2019, ICMAI 2019.

[35]  Toon De Pessemier,et al.  Hybrid group recommendations for a travel service , 2016, Multimedia Tools and Applications.

[36]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[37]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[38]  Min Chen,et al.  iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization , 2017, Future Gener. Comput. Syst..

[39]  Ville Ollikainen,et al.  A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data , 2015, Knowl. Based Syst..

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

[41]  Hwanjo Yu,et al.  Deep hybrid recommender systems via exploiting document context and statistics of items , 2017, Inf. Sci..

[42]  Maoqiang Xie,et al.  Collaborative linear manifold learning for link prediction in heterogeneous networks , 2020, Inf. Sci..

[43]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[44]  Arun Kumar Sangaiah,et al.  LeaderRank based k-means clustering initialization method for collaborative filtering , 2017, Comput. Electr. Eng..

[45]  Franca Garzotto,et al.  Content-Based Video Recommendation System Based on Stylistic Visual Features , 2016, Journal on Data Semantics.

[46]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[47]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[48]  Chitra Dadkhah,et al.  Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm , 2020 .

[49]  Hafed Zarzour,et al.  An Effective Recommender System Based on Clustering Technique for TED Talks , 2020, Int. J. Inf. Technol. Web Eng..