An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

[1]  Cheng Xiao-ping.  Item-based collaborative filtering algorithm using attribute similarity , 2007 .

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

[3]  Xiao Fu,et al.  Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System , 2017 .

[4]  Katrien Verbert,et al.  Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities , 2016, Expert Syst. Appl..

[5]  Ludovico Bullini Orlandi,et al.  Organizational capabilities in the digital era: Reframing strategic orientation , 2016 .

[6]  Pradeep Kumar,et al.  A web recommendation system considering sequential information , 2015, Decis. Support Syst..

[7]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[8]  Erik Duval,et al.  Visualizing recommendations to support exploration, transparency and controllability , 2013, IUI '13.

[9]  Thomas Hess,et al.  Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions: An Experimental Study , 2012, J. Manag. Inf. Syst..

[10]  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.

[11]  George A. Tsihrintzis,et al.  Evaluation of a Cascade Hybrid Recommendation as a Combination of One-Class Classification and Collaborative Filtering , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[12]  Jordi Forné,et al.  On content-based recommendation and user privacy in social-tagging systems , 2015, Comput. Stand. Interfaces.

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

[14]  Kirsten Swearingen,et al.  Beyond Algorithms: An HCI Perspective on Recommender Systems , 2001 .

[15]  Yi-Cheng Ku,et al.  A semantic-expansion approach to personalized knowledge recommendation , 2008, Decis. Support Syst..

[16]  Pei-Chann Chang,et al.  Applying artificial immune systems to collaborative filtering for movie recommendation , 2015, Adv. Eng. Informatics.

[17]  Alexander Felfernig,et al.  Knowledge-Based Recommender Technologies for Marketing and Sales , 2007, Int. J. Pattern Recognit. Artif. Intell..

[18]  Tim Hussein,et al.  Hybreed: A software framework for developing context-aware hybrid recommender systems , 2012, User Modeling and User-Adapted Interaction.

[19]  Jianguo Zheng,et al.  A collaborative filtering recommendation algorithm based on improved similarity measure method , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

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

[21]  Lun-Ping Hung,et al.  A personalized recommendation system based on product taxonomy for one-to-one marketing online , 2005, Expert Syst. Appl..

[22]  Sansanee Auephanwiriyakul,et al.  Top-N Recommender Systems Using Genetic Algorithm-Based Visual-Clustering Methods , 2016, Symmetry.

[23]  Mehrbakhsh Nilashi,et al.  A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS , 2015, Electron. Commer. Res. Appl..

[24]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[25]  James T. Kwok,et al.  Mining customer product ratings for personalized marketing , 2003, Decis. Support Syst..

[26]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[27]  Xin Li,et al.  Application of an improved Apriori algorithm in a mobile e-commerce recommendation system , 2017, Ind. Manag. Data Syst..

[28]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[29]  Troy A. Voelker,et al.  Internet presence as a small business capability: the case of mobile optimization , 2017 .

[30]  Li Jun,et al.  An Improved Apriori Algorithm Based On the Boolean Matrix and Hadoop , 2011 .

[31]  Anthony F. Norcio,et al.  Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems , 2009, Fuzzy Sets Syst..

[32]  Fadi A. Thabtah,et al.  A review of associative classification mining , 2007, The Knowledge Engineering Review.

[33]  Sungchang Lee,et al.  Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks , 2016, Symmetry.

[34]  Helena Alves,et al.  Customer's operant resources effects on co-creation activities , 2016 .

[35]  Arvind K. Tripathi,et al.  Design of a shopbot and recommender system for bundle purchases , 2006, Decis. Support Syst..

[36]  Michael Scholz,et al.  Measuring consumers' willingness to pay with utility-based recommendation systems , 2015, Decis. Support Syst..

[37]  Sungchang Lee,et al.  N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering , 2014, TheScientificWorldJournal.

[38]  Nick Antonopoulos,et al.  CinemaScreen recommender agent: combining collaborative and content-based filtering , 2006, IEEE Intelligent Systems.

[39]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[40]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[41]  Paul Jen-Hwa Hu,et al.  A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce , 2012, Decis. Support Syst..

[42]  Ahmad A. Kardan,et al.  A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups , 2013, Inf. Sci..

[43]  Olivia R. Liu Sheng,et al.  Utility-Based Link Recommendation for Online Social Networks , 2015, Manag. Sci..

[44]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[45]  Jongwuk Lee,et al.  Improving the accuracy of top-N recommendation using a preference model , 2016, Inf. Sci..