A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

[1]  Dietmar Jannach,et al.  News recommender systems - Survey and roads ahead , 2018, Inf. Process. Manag..

[2]  George C. Banks,et al.  A Review of Best Practice Recommendations for Text Analysis in R (and a User-Friendly App) , 2018 .

[3]  Shoubin Dong,et al.  An Effective News Recommendation Method for Microblog User , 2014, TheScientificWorldJournal.

[4]  Qing Li,et al.  HCRS: A hybrid clothes recommender system based on user ratings and product features , 2014, ArXiv.

[5]  Vishal Gupta,et al.  A systematic review of text stemming techniques , 2016, Artificial Intelligence Review.

[6]  Juan Manuel Cueva Lovelle,et al.  Creating recommendations on electronic books: A collaborative learning implicit approach , 2015, Comput. Hum. Behav..

[7]  Ho-Jin Choi,et al.  User profile extraction from Twitter for personalized news recommendation , 2014, 16th International Conference on Advanced Communication Technology.

[8]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[9]  Abdelghani Souhar,et al.  Spatial and Textural Aspects for Arabic Handwritten Characters Recognition , 2018, Int. J. Interact. Multim. Artif. Intell..

[10]  Jose María Álvarez Rodríguez,et al.  Towards a journalist-based news recommendation system: The Wesomender approach , 2013, Expert Syst. Appl..

[11]  Eamonn J. Keogh,et al.  Curse of Dimensionality , 2010, Encyclopedia of Machine Learning.

[12]  Vincent P. Wade,et al.  Personalised Information Retrieval: survey and classification , 2013, User Modeling and User-Adapted Interaction.

[13]  Pablo Moreno-Ger,et al.  Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets , 2018, Int. J. Interact. Multim. Artif. Intell..

[14]  Hamid Hassanpour,et al.  User trends modeling for a content-based recommender system , 2017, Expert Syst. Appl..

[15]  Bin Wang,et al.  How do product recommendations affect impulse buying? An empirical study on WeChat social commerce , 2019, Inf. Manag..

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

[17]  Na Jiang,et al.  Implicit Feedback Recommender System Based on Matrix Factorization , 2018, TRIDENTCOM 2018.

[18]  Jianxing Zheng,et al.  Personalized Recommendations Based on Sentimental Interest Community Detection , 2018, Sci. Program..

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

[20]  Rik Van de Walle,et al.  Automatic news recommendations via aggregated profiling , 2011, Multimedia Tools and Applications.

[21]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[22]  Xia Feng,et al.  Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey , 2017, Multimedia Tools and Applications.

[23]  Tao Li,et al.  PENETRATE: Personalized news recommendation using ensemble hierarchical clustering , 2013, Expert Syst. Appl..

[24]  Jun Wen,et al.  Text Categorization Based on a Similarity Approach , 2007 .

[25]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[26]  Fan Yang,et al.  Modeling and broadening temporal user interest in personalized news recommendation , 2014, Expert Syst. Appl..

[27]  Morteza Analoui,et al.  Analiysis of self-similarity in recommender systems , 2014, 2014 Iranian Conference on Intelligent Systems (ICIS).

[28]  Klaus-Robert Müller,et al.  Discovering topics in text datasets by visualizing relevant words , 2017, ArXiv.

[29]  Mohsen Rahmani,et al.  Performance improvement for recommender systems using ontology , 2017, Telematics Informatics.

[30]  Kuan-Ching Li,et al.  A keyword-aware recommender system using implicit feedback on Hadoop , 2018, J. Parallel Distributed Comput..

[31]  Tao Li,et al.  News recommendation via hypergraph learning: encapsulation of user behavior and news content , 2013, WSDM.

[32]  Rahul Katarya.  Movie recommender system with metaheuristic artificial bee , 2017, Neural Computing and Applications.

[33]  Victor Chang,et al.  Efficient method for feature selection in text classification , 2017, 2017 International Conference on Engineering and Technology (ICET).

[34]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[35]  Xinwei Wang,et al.  Text feature extraction based on joint conditional entropy , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[36]  Susan Gauch,et al.  Incorporating popularity in a personalized news recommender system , 2016, PeerJ Comput. Sci..

[37]  Daniel Herzog,et al.  Combining Content-based and Collaborative Filtering for Personalized Sports News Recommendations , 2016, CBRecSys@RecSys.

[38]  Sung Joo Park,et al.  MONERS: A news recommender for the mobile web , 2007, Expert Syst. Appl..

[39]  Vasant Honavar,et al.  A user similarity-based Top-N recommendation approach for mobile in-application advertising , 2018, Expert Syst. Appl..

[40]  Archana Singhal,et al.  Handling skewed results in news recommendations by focused analysis of semantic user profiles , 2014, 2014 International Conference on Reliability Optimization and Information Technology (ICROIT).

[41]  Mahdi Jalili.  A survey of collaborative filtering recommender algorithms and their evaluation metrics , 2017 .

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

[43]  Yiming Cao,et al.  Scalable news recommendation using multi-dimensional similarity and Jaccard-Kmeans clustering , 2014, J. Syst. Softw..

[44]  Mark Claypool,et al.  Implicit interest indicators , 2001, IUI '01.

[45]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[46]  Luis F. Chiroque,et al.  Graph-based Techniques for Topic Classification of Tweets in Spanish , 2014, Int. J. Interact. Multim. Artif. Intell..

[47]  Benno Stein,et al.  The ESA retrieval model revisited , 2009, SIGIR.

[48]  David Contreras,et al.  EventAware: A mobile recommender system for events , 2017, Pattern Recognit. Lett..

[49]  Enrique Herrera-Viedma,et al.  A recommender system based on implicit feedback for selective dissemination of ebooks , 2018, Inf. Sci..

[50]  Seunghee Lee,et al.  A study on consumers’ experiences and avoidances of mobile shopping application advertisements , 2018 .

[51]  Antonio Moreno,et al.  Text Analytics: the convergence of Big Data and Artificial Intelligence , 2016, Int. J. Interact. Multim. Artif. Intell..

[52]  Alok Ranjan Pal,et al.  A comparison between extrinsic and intrinsic technique for multi-document text summarization , 2017, 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[53]  Bela Gipp,et al.  Research-paper recommender systems: a literature survey , 2015, International Journal on Digital Libraries.

[54]  Matevz Kunaver,et al.  Diversity in recommender systems - A survey , 2017, Knowl. Based Syst..

[55]  J. R. Lucas,et al.  Types and tokens , 2018, A Treatise on Time and Space.

[56]  Gong Ling,et al.  An improved TF-IDF approach for text classification , 2005 .

[57]  Guy Shani,et al.  Tutorial on application-oriented evaluation of recommendation systems , 2013, AI Commun..