Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems

Provides a hybrid methodology for alleviating the cold start.Evaluates some of the most well-known probability metrics for scoring rules.Develops a novel scoring function for ranking rules.Exploits semantic web advantages (e.g. reusability, interoperability, etc.)A qualitative rule set which can be used by other services is created. Successful Location-Based Services should offer accurate and timely information consumption recommendations to their customers, relevant to their contextual situation. To achieve this and provide the best available recommendations to the user, researchers and developers analyse available data via exploiting data mining techniques. Unfortunately, in some cases, due to lack of available data (e.g. a relatively new member with a limited history) the above technologies and methods are not very effective. In this paper, a novel hybrid approach to alleviate the above-mentioned problem, known as cold start in context-aware recommender systems, is presented. This work aims to help researchers and developers to cope with this problem by combining a) community created knowledge, b) ontologies c) association rule mining and d) an innovative scoring function based on probability metrics.

[1]  Yue Xu,et al.  Using Association Rules to Solve the Cold-Start Problem in Recommender Systems , 2010, PAKDD.

[2]  F. O. Isinkaye,et al.  Recommendation systems: Principles, methods and evaluation , 2015 .

[3]  Hans Weigand,et al.  Rule-based service composition and service-oriented business rule management , 2008 .

[4]  Nick Bassiliades,et al.  Using Rules to Develop a Personalized and Social Location Information System for the Semantic Web , 2014, RuleML.

[5]  Paulo S. C. Alencar,et al.  The use of machine learning algorithms in recommender systems: A systematic review , 2015, Expert Syst. Appl..

[6]  Ricardo Colomo Palacios,et al.  Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain , 2011, Expert Syst. Appl..

[7]  William Zhu,et al.  Cold-start recommendation through granular association rules , 2013, ArXiv.

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

[9]  Yuhua Qian,et al.  Multi-level preference regression for cold-start recommendations , 2018, Int. J. Mach. Learn. Cybern..

[10]  Raz Tamir,et al.  On a confidence gain measure for association rule discovery and scoring , 2004, The VLDB Journal.

[11]  Duane Kimbell Fields,et al.  Web Development with Java Server Pages , 2001 .

[12]  Xing Xie,et al.  Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity , 2014, Pervasive Mob. Comput..

[13]  Hong Cheng,et al.  A Monte Carlo algorithm for cold start recommendation , 2014, WWW.

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

[15]  Neha Sharma,et al.  Comparative Survey on Association Rule Mining Algorithms , 2013 .

[16]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[17]  Mehrbakhsh Nilashi,et al.  A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques , 2018, Expert Syst. Appl..

[18]  Kenneth McGarry,et al.  A survey of interestingness measures for knowledge discovery , 2005, The Knowledge Engineering Review.

[19]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[20]  Hao Tian,et al.  Personalized Service Recommendation Based on Trust Relationship , 2017, Sci. Program..

[21]  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.

[22]  Oren Somekh,et al.  ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations , 2015, RecSys.

[23]  Nick Bassiliades,et al.  A context-aware web-mapping system for group-targeted offers using semantic technologies , 2015, Expert Syst. Appl..

[24]  Mohammad-Hossein Nadimi-Shahraki,et al.  Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique , 2014, J. Comput. Inf. Technol..

[25]  Wesley W. Chu,et al.  A social network-based recommender system , 2010 .

[26]  Yogesh Ghodasara,et al.  Using Apriori with WEKA for Frequent Pattern Mining , 2014, ArXiv.

[27]  Krzysztof Janowicz,et al.  On the semantic annotation of places in location-based social networks , 2011, KDD.

[28]  Ernest Friedman-Hill,et al.  Jess in action : rule-based systems in Java , 2003 .

[29]  Suchismit Mahapatra,et al.  A Cold Start Recommendation System Using Item Correlation and User Similarity , 2011 .

[30]  David Carmel,et al.  Social media recommendation based on people and tags , 2010, SIGIR.

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

[32]  Beatrice Lazzerini,et al.  Situation-Aware Mobile Service Recommendation with Fuzzy Logic and Semantic Web , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[33]  John Riedl,et al.  Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.

[34]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[35]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[36]  Hridya Sobhanam,et al.  A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique , 2013 .

[37]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[38]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[39]  Jaime Raigoza,et al.  A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment , 2017, Int. J. Grid High Perform. Comput..

[40]  Le Hoang Son.  Dealing with the new user cold-start problem in recommender systems: A comparative review , 2016, Inf. Syst..

[41]  Christos Tjortjis,et al.  The 50/50 Recommender: A Method Incorporating Personality into Movie Recommender Systems , 2017, EANN.

[42]  Maria Papadopouli,et al.  A Semantics-Based Framework for Context-Aware Services: Lessons Learned and Challenges , 2007, UIC.

[43]  Santosh V. Chobe,et al.  An Overview of Association Rule Mining Algorithms , 2014 .

[44]  Hanghang Tong,et al.  Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison , 2015, IJCAI.

[45]  Scott Sanner,et al.  Social collaborative filtering for cold-start recommendations , 2014, RecSys '14.

[46]  Erik Duval,et al.  Context-Aware Recommender Systems for Learning: A Survey and Future Challenges , 2012, IEEE Transactions on Learning Technologies.

[47]  Korris Fu-Lai Chung,et al.  An empirical study of a cross-level association rule mining approach to cold-start recommendations , 2008, Knowl. Based Syst..

[48]  Weiyang Lin.  Association Rule Mining for Collaborative Recommender Systems , 2000 .

[49]  Sourav S. Bhowmick,et al.  Association Rule Mining: A Survey , 2003 .

[50]  Daqing Zhang,et al.  Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[51]  Christophe Claramunt,et al.  A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning , 2017, Mob. Inf. Syst..

[52]  D L Sackett,et al.  Why randomized controlled trials fail but needn't: a new series is launched. , 2000, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[53]  Christophe Diot,et al.  Finding a needle in a haystack of reviews: cold start context-based hotel recommender system , 2012, RecSys.

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

[55]  Stathes Hadjiefthymiades,et al.  Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..

[56]  Isabelle Tellier,et al.  Reducing the Cold-Start Problem in Content Recommendation through Opinion Classification , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[57]  William W. Cohen,et al.  Community-Based Recommendations: a Solution to the Cold Start Problem , 2011 .

[58]  Chen Meng,et al.  A Method to Solve Cold-Start Problem in Recommendation System based on Social Network Sub-community and Ontology Decision Model , 2013, ICMT 2013.

[59]  Aitor Almeida,et al.  Alleviating cold-user start problem with users ’ social network data in recommendation systems , 2012 .

[60]  Adrian Giurca,et al.  RuleTheWeb!: Rule-based Adaptive User Experience , 2012, RuleML.

[61]  Carsten Keßler.  A RESTful SWRL Rule Editor , 2010, RR.

[62]  Amor Lazzez,et al.  Efficient Analysis of Pattern and Association Rule Mining Approaches , 2014, ArXiv.

[63]  Naomie Salim,et al.  Recommendation systems: a review , 2013 .

[64]  Nick Bassiliades,et al.  Modeling human daily preferences through a context-aware web-mapping system using semantic technologies , 2017, Pervasive Mob. Comput..

[65]  D L Sackett,et al.  Why randomized controlled trials fail but needn't: 2. Failure to employ physiological statistics, or the only formula a clinician-trialist is ever likely to need (or understand!). , 2001, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[66]  Hong Shen,et al.  Addressing cold-start: Scalable recommendation with tags and keywords , 2015, Knowl. Based Syst..

[67]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[68]  Judith Masthoff,et al.  Explaining Recommendations: Design and Evaluation , 2015, Recommender Systems Handbook.

[69]  Jiebo Luo,et al.  Home Location Inference from Sparse and Noisy Data: Models and Applications , 2015, ICDM Workshops.