Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.

[1]  Maoguo Gong,et al.  NNIA-RS: A multi-objective optimization based recommender system , 2015 .

[2]  Shin Min Kang,et al.  ON MORE TOPOLOGICAL INDICES OF JAHANGIR GRAPHS , 2018 .

[3]  Jun Wang,et al.  Optimizing multiple objectives in collaborative filtering , 2010, RecSys '10.

[4]  Osmar R. Zaïane,et al.  Building a Recommender Agent for e-Learning Systems , 2002, ICCE.

[5]  Jyun-Cheng Wang,et al.  Recommending trusted online auction sellers using social network analysis , 2008, Expert Syst. Appl..

[6]  Weimin Pan,et al.  An improved collaborative filtering algorithm combining content-based algorithm and user activity , 2014, 2014 International Conference on Big Data and Smart Computing (BIGCOMP).

[7]  Gediminas Adomavicius,et al.  Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques , 2012, IEEE Transactions on Knowledge and Data Engineering.

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

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

[10]  Yi-Cheng Zhang,et al.  Recommender Systems , 2012, ArXiv.

[11]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[12]  Bing-Hong Wang,et al.  Accurate and diverse recommendations via eliminating redundant correlations , 2008, 0805.4127.

[13]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[14]  Adriano Veloso,et al.  Pareto-efficient hybridization for multi-objective recommender systems , 2012, RecSys.

[15]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..

[16]  Maoguo Gong,et al.  Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier] , 2015, IEEE Computational Intelligence Magazine.

[17]  Jun Zhang,et al.  An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm , 2017, IEEE Transactions on Cybernetics.

[18]  Jie Zhang,et al.  Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

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

[20]  Sebastián Ventura,et al.  Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems , 2009, Comput. Educ..

[21]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[22]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[23]  Andreas Nürnberger,et al.  Research paper recommender system evaluation: a quantitative literature survey , 2013, RepSys '13.

[24]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

[27]  Maoguo Gong,et al.  Reliable Link Inference for Network Data With Community Structures , 2019, IEEE Transactions on Cybernetics.

[28]  Jun Zhang,et al.  A Diversity-Enhanced Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithm , 2018, IEEE Transactions on Cybernetics.

[29]  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.

[30]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[31]  Jinghua Huang,et al.  A Survey of E-Commerce Recommender Systems , 2007, 2007 International Conference on Service Systems and Service Management.

[32]  Yang Guo-xing K-Means Clustering Analysis Based on Genetic Algorithm , 2008 .

[33]  Jun Zhang,et al.  Cloudde: A Heterogeneous Differential Evolution Algorithm and Its Distributed Cloud Version , 2017, IEEE Transactions on Parallel and Distributed Systems.

[34]  Maoguo Gong,et al.  Multi-objective optimization for long tail recommendation , 2016, Knowl. Based Syst..

[35]  Hendrik Drachsler,et al.  Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model , 2008, Int. J. Learn. Technol..

[36]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[37]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[38]  Mi Zhang,et al.  Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.

[39]  Mohammad Yahya H. Al-Shamri,et al.  User profiling approaches for demographic recommender systems , 2016, Knowl. Based Syst..

[40]  A. R. Baig,et al.  Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm , 2015 .

[41]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[42]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[43]  Jie Lu,et al.  A Personalized e-Learning material Recommender System , 2004 .

[44]  Takao Terano,et al.  Improving the Performance of User-Based Collaborative Filtering by Mining Latent Attributes of Neighborhood , 2014, 2014 International Conference on Mathematics and Computers in Sciences and in Industry.

[45]  Laizhong Cui,et al.  A novel multi-objective evolutionary algorithm for recommendation systems , 2017, J. Parallel Distributed Comput..

[46]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[47]  Rinky Goyal,et al.  A Three Way Hybrid Movie Recommendation System , 2017 .

[48]  Rama Chellappa,et al.  An electronic infrastructure for a virtual university , 1997, CACM.

[49]  Karan Soni,et al.  A Three Way Hybrid Movie Recommendation Syste , 2017 .