A Recommender System With IBA Similarity Measure

Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coefficient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases.

[1]  Dragan G. Radojevic,et al.  IBA-based framework for modeling similarity , 2018, Int. J. Comput. Intell. Syst..

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

[3]  Dragan G. Radojevic,et al.  Modeling consensus using logic-based similarity measures , 2015, Soft Comput..

[4]  Dragan G. Radojevic,et al.  A Consensus Model in Group Decision Making Based on Interpolative Boolean Algebra , 2013, EUSFLAT Conf..

[5]  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..

[6]  Yin Zhang,et al.  An alternating direction algorithm for matrix completion with nonnegative factors , 2011, Frontiers of Mathematics in China.

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

[8]  Hong Yan,et al.  Recommender systems based on social networks , 2015, J. Syst. Softw..

[9]  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.

[10]  Dragan Radojevic,et al.  [0,1]-VALUED LOGIC: A NATURAL GENERALIZATION OF BOOLEAN LOGIC , 2000 .

[11]  Sang-goo Lee,et al.  An Ontology-Based Product Recommender System for B2B Marketplaces , 2006, Int. J. Electron. Commer..

[12]  Hua Wang,et al.  Evolutionary heterogeneous clustering for rating prediction based on user collaborative filtering , 2018, Swarm Evol. Comput..

[13]  Gleb Beliakov,et al.  Consensus measures constructed from aggregation functions and fuzzy implications , 2014, Knowl. Based Syst..

[14]  Dragan Radojevic.  Logical aggregation based on interpolative , 2008, SOCO 2008.

[15]  Mehrbakhsh Nilashi,et al.  Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system , 2014, Knowl. Based Syst..

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

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

[18]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

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

[20]  Mohsen Ramezani,et al.  A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains , 2014 .

[21]  Marc Boullé,et al.  Comparing State-of-the-Art Collaborative Filtering Systems , 2007, MLDM.

[22]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[23]  Hoel Le Capitaine,et al.  A Relevance-Based Learning Model of Fuzzy Similarity Measures , 2012, IEEE Transactions on Fuzzy Systems.

[24]  Sangwon Lee,et al.  Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network , 2017, Inf. Process. Manag..

[25]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[26]  Rahul Katarya,et al.  An effective collaborative movie recommender system with cuckoo search , 2017 .

[27]  D. Radojevic,et al.  Interpolative Realization of Boolean Algebra as a Consistent Frame for Gradation and/or Fuzziness , 2008 .

[28]  Chih-Fong Tsai,et al.  Cluster ensembles in collaborative filtering recommendation , 2012, Appl. Soft Comput..

[29]  Daniel Thalmann,et al.  ETAF: An extended trust antecedents framework for trust prediction , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[30]  Huan Liu,et al.  Research Paper Recommender Systems: A Subspace Clustering Approach , 2005, WAIM.

[31]  Kourosh Kiani,et al.  A new method to find neighbor users that improves the performance of Collaborative Filtering , 2017, Expert Syst. Appl..