Personalized Hybrid Book Recommender

PersonalizedRecommendationSystems(RS)provideenduserswithsuggestionsaboutitemsthatare likelytobeoftheirinterestbasedonusers’detailssuchasdemographics,location,time,andemotion. Inthisarticle,aPersonalizedHybridBookRecommender(PHyBR)ispresented,whichintegrates personalitytraitswithusers’demographicdataandgeographicallocationtoimprovethequalityof recommendations.TheTenItemPersonalityInventory(TIPI)wasusedtodetermineusers’personality traits.PHyBRwasevaluatedusingtwometrics,thatare,StandardizedRootMeanSquareResidual (SRMR)andRootMeanSquareErrorofApproximation(RMSEA).BothmetricsrevealedPHyBR outperformsthebaselinemodels(withoutconsideringpersonalitytraitsandgeographicallocation factor)intermsoftherecommendationaccuracies.Thisstudyshowsthatuserswhoareinthesame geographicalcontextsintendtohavesimilarpreferences.Therefore,users’personalitydetailsalong withtheirgeographicallocationscanbeusedtoprovideimprovedpersonalizedrecommendations. KeywoRdS Age, Big Five, Collaborative Filtering, Content-Based Filtering, Gender, Location, Personality, Recommendation System

[1]  Ahmed E. Hassan,et al.  Examining the Rating System Used in Mobile-App Stores , 2016, IEEE Software.

[2]  Charalampos Konstantopoulos,et al.  A survey on algorithmic approaches for solving tourist trip design problems , 2014, Journal of Heuristics.

[3]  Anand Shanker Tewari,et al.  Collaborative book recommendation system using trust based social network and association rule mining , 2016, 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I).

[4]  Reena Pagare,et al.  Study of Collaborative Filtering Recommendation Algorithm - Scalability Issue , 2013 .

[5]  P. Costa,et al.  Toward a new generation of personality theories: Theoretical contexts for the five-factor model. , 1996 .

[6]  Nana Yaw Asabere,et al.  Improving Socially-Aware Recommendation Accuracy Through Personality , 2018, IEEE Transactions on Affective Computing.

[7]  Huan Liu,et al.  Content-Aware Point of Interest Recommendation on Location-Based Social Networks , 2015, AAAI.

[8]  Mark Bilandzic,et al.  Towards Participatory Action Design Research: Adapting Action Research and Design Science Research Methods for Urban Informatics , 2011, J. Community Informatics.

[9]  Christian Huemer,et al.  E-Commerce and Web Technologies - 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011. Proceedings , 2011, EC-Web.

[10]  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..

[11]  P. Corr.  Reinforcement Sensitivity Theory of Personality Questionnaires: Structural survey with recommendations , 2016 .

[12]  Zhongfu Wu,et al.  Personalisation in web computing and informatics: Theories, techniques, applications, and future research , 2010, Inf. Syst. Frontiers.

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

[14]  Michael R. Lyu,et al.  A Survey of Point-of-interest Recommendation in Location-based Social Networks , 2016, ArXiv.

[15]  Chih-Ming Chen,et al.  An intelligent mobile location-aware book recommendation system that enhances problem-based learning in libraries , 2013, Interact. Learn. Environ..

[16]  Marios Poulos,et al.  Using Online Consumer Reviews as a Source for Demographic Recommendations: A Case Study Using Online Travel Reviews , 2013, Expert Syst. Appl..

[17]  Wei Pang,et al.  Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations , 2014, 2014 IEEE International Conference on Data Mining.

[18]  Saumya Chaturvedi,et al.  Solving Sparsity Problem in Rating-Based Movie Recommendation System , 2017 .

[19]  Lei Cen,et al.  Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference , 2015, WSDM.

[20]  Thorsten Hennig-Thurau,et al.  Can Automated Group Recommender Systems Help Consumers Make Better Choices? , 2012 .

[21]  Worapat Paireekreng,et al.  Mobile content recommendation system for re-visiting user using content-based filtering and client-side user profile , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[22]  Richard Baskerville,et al.  Eating Our Own Cooking: Toward a More Rigorous Design Science of Research Methods , 2012 .

[23]  Veda C. Storey,et al.  Design science in the information systems discipline: an introduction to the special issue on design science research , 2008 .

[24]  Sandeep Purao,et al.  Action Design Research , 2011, MIS Q..

[25]  Shivani Bhosale,et al.  SuggestABook: A Book Recommender Engine with Personality based Mapping , 2017 .

[26]  Rafael Valencia-García,et al.  Solving the cold-start problem in recommender systems with social tags , 2010, Expert Syst. Appl..

[27]  Li Chen,et al.  Recommender systems based on user reviews: the state of the art , 2015, User Modeling and User-Adapted Interaction.

[28]  Maria Soledad Pera,et al.  Personalized Book Recommendations Created by Using Social Media Data , 2010, WISE Workshops.

[29]  Kinshuk,et al.  Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support , 2011, Comput. Educ..

[30]  Mohamed F. Mokbel,et al.  Location-based and preference-aware recommendation using sparse geo-social networking data , 2012, SIGSPATIAL/GIS.

[31]  Alan R. Hevner,et al.  The Three Cycle View of Design Science , 2007, Scand. J. Inf. Syst..

[32]  Suh-Yin Lee,et al.  Emotion-based music recommendation by affinity discovery from film music , 2009, Expert Syst. Appl..

[33]  Mojtaba Salehi,et al.  Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree , 2013, Knowl. Based Syst..

[34]  Fernando Ortega,et al.  A collaborative filtering approach to mitigate the new user cold start problem , 2012, Knowl. Based Syst..

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

[36]  D. Betsy McCoach,et al.  The Performance of RMSEA in Models With Small Degrees of Freedom , 2015 .

[37]  Paul Jen-Hwa Hu,et al.  A Web-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation , 2010, Expert Syst. Appl..

[38]  Bruce Ferwerda,et al.  Enhancing Music Recommender Systems with Personality Information and Emotional States: A Proposal , 2014, UMAP Workshops.

[39]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[40]  Francesco Ricci,et al.  STS: A Context-Aware Mobile Recommender System for Places of Interest , 2014, UMAP Workshops.

[41]  María N. Moreno García,et al.  A hybrid recommendation approach for a tourism system , 2013, Expert Syst. Appl..

[42]  Zibin Zheng,et al.  Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation , 2016, IEEE Transactions on Services Computing.

[43]  J. Hofmans,et al.  Is short in length short in content? An examination of the domain representation of the Ten Item Personality Inventory scales in Dutch language , 2008 .

[44]  Kai Chen,et al.  Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..

[45]  Tiffany Ya Tang,et al.  The role of user mood in movie recommendations , 2010, Expert Syst. Appl..

[46]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[47]  Haosheng Huang,et al.  Context-Aware Location Recommendation Using Geotagged Photos in Social Media , 2016, ISPRS Int. J. Geo Inf..

[48]  Lichao Feng,et al.  Collaborative filtering model of book recommendation system , 2016 .

[49]  Joon-Min Gil,et al.  Design and Implementation of MapReduce-Based Book Recommendation System by Analysis of Large-Scale Book-Rental Data , 2016 .

[50]  Iván Cantador,et al.  Alleviating the new user problem in collaborative filtering by exploiting personality information , 2016, User Modeling and User-Adapted Interaction.

[51]  Juan Manuel Cueva Lovelle,et al.  Implicit feedback techniques on recommender systems applied to electronic books , 2012, Comput. Hum. Behav..

[52]  Li Chen,et al.  Generating virtual ratings from chinese reviews to augment online recommendations , 2013, TIST.

[53]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[54]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[55]  Charalampos Konstantopoulos,et al.  Mobile recommender systems in tourism , 2014, J. Netw. Comput. Appl..

[56]  Tinghuai Ma,et al.  Social Network and Tag Sources Based Augmenting Collaborative Recommender System , 2015, IEICE Trans. Inf. Syst..

[57]  F. García.  Effect-Size Reporting in Mexican Psychology Journals: What it Says about the Quality of Research within the Field , 2017 .

[58]  Nathalie Blanc,et al.  Improving Recommendations by Using Personality Traits in User Profiles , 2008 .

[59]  Deborah Bunker,et al.  Human Benefit through the Diffusion of Information Systems Design Science Research - IFIP WG 8.2/8.6 International Working Conference, Perth, Australia, March 30 - April 1, 2010. Proceedings , 2010, Human Benefit through the Diffusion of Information Systems Design Science Research.

[60]  Yuexin Wu,et al.  We know what you want to buy: a demographic-based system for product recommendation on microblogs , 2014, KDD.

[61]  Rajani Chulyadyo,et al.  A Personalized Recommender System from Probabilistic Relational Model and Users' Preferences , 2014, KES.

[62]  Guandong Xu,et al.  Social network-based service recommendation with trust enhancement , 2014, Expert Syst. Appl..

[63]  Alfred Kobsa,et al.  Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems , 2013, TIIS.

[64]  Huan Zhou.  A New-come Book Recommendation Algorithm Based on Features in University‘s Library , 2015 .

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

[66]  Joshua J. Jackson,et al.  When Friends’ and Society’s Expectations Collide: A Longitudinal Study of Moral Decision-Making and Personality across College , 2016, PloS one.

[67]  Rong Hu,et al.  A Study on User Perception of Personality-Based Recommender Systems , 2010, UMAP.

[68]  Akram Salah,et al.  Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System , 2013 .

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

[70]  GeunSik Jo,et al.  Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation , 2009, 2009 First Asian Conference on Intelligent Information and Database Systems.

[71]  Alejandro Bellogín,et al.  Relating Personality Types with User Preferences in Multiple Entertainment Domains , 2013, UMAP Workshops.

[72]  Hagar Mahmoud,et al.  Shortest Path Calculation: A Comparative Study for Location-Based Recommender System , 2016, 2016 World Symposium on Computer Applications & Research (WSCAR).

[73]  Francesco Ricci,et al.  Mobile Recommender Systems , 2010, J. Inf. Technol. Tour..

[74]  Fuli Zhang,et al.  A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries , 2016, IEEE Access.

[75]  Francesco Ricci,et al.  Personality-Based Active Learning for Collaborative Filtering Recommender Systems , 2013, AI*IA.

[76]  Maria Fasli,et al.  Utilizing contextual ontological user profiles for personalized recommendations , 2014, Expert Syst. Appl..

[77]  Pei-Chann Chang,et al.  Application of artificial immune systems combines collaborative filtering in movie recommendation system , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[78]  Mehrbakhsh Nilashi,et al.  Hybrid recommendation approaches for multi-criteria collaborative filtering , 2014, Expert Syst. Appl..

[79]  Yi Li,et al.  A hybrid recommendation algorithm adapted in e-learning environments , 2012, World Wide Web.

[80]  Om Prakash Verma,et al.  Personalized hybrid book recommender system using neural network , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[81]  Gresha Bhatia,et al.  Web-based personalized hybrid book recommendation system , 2014, 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014).

[82]  Pornpen Ratsameethammawong,et al.  Mobile Phone Location Tracking by the Combination of GPS, Wi-Fi and Cell Location Technology , 2010 .

[83]  Saso Koceski,et al.  Empirical Evidence of Contribution to E-Tourism by Application of Personalized Tourism Recommendation System , 2012 .

[84]  Pasquale Lops,et al.  Human Decision Making and Recommender Systems , 2013, TIIS.

[85]  Xinbo Gao,et al.  Attraction recommendation: Towards personalized tourism via collective intelligence , 2016, Neurocomputing.

[86]  Feng-Hsu Wang,et al.  Effective personalized recommendation based on time-framed navigation clustering and association mining , 2004, Expert Syst. Appl..

[87]  Vladeta Ajdacic-Gross,et al.  Big Five personality traits may inform public health policy and preventive medicine: Evidence from a cross-sectional and a prospective longitudinal epidemiologic study in a Swiss community. , 2016, Journal of psychosomatic research.