A hybrid recommender system for recommending relevant movies using an expert system

Abstract Currently, the Internet contains a large amount of information, which must then be filtered to determine suitability for certain users. Recommender systems are a very suitable tool for this purpose. In this paper, we propose a monolithic hybrid recommender system called Predictory, which combines a recommender module composed of a collaborative filtering system (using the SVD algorithm), a content-based system, and a fuzzy expert system. The proposed system serves to recommend suitable movies. The system works with favorite and unpopular genres of the user, while the final list of recommended movies is determined using a fuzzy expert system, which evaluates the importance of the movies. The expert system works with several parameters – average movie rating, number of ratings, and the level of similarity between already rated movies. Therefore, our system achieves better results than traditional approaches, such as collaborative filtering systems, content-based systems, and weighted hybrid systems. The system verification based on standard metrics (precision, recall, F1-measure) achieves results over 80%. The main contribution is the creation of a complex hybrid system in the area of movie recommendation, which has been verified on a group of users using the MovieLens dataset and compared with other traditional recommender systems.

[1]  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.

[2]  Panagiotis Symeonidis,et al.  MoviExplain: a recommender system with explanations , 2009, RecSys '09.

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

[4]  Ricardo B. C. Prudêncio,et al.  A literature review of recommender systems in the television domain , 2015, Expert Syst. Appl..

[5]  Stanley Loh,et al.  A Tourism Recommender System Based on Collaboration and Text Analysis , 2003, J. Inf. Technol. Tour..

[6]  Xavier Serra,et al.  Sound and Music Recommendation with Knowledge Graphs , 2016, ACM Trans. Intell. Syst. Technol..

[7]  George Lekakos,et al.  A hybrid approach for movie recommendation , 2006, Multimedia Tools and Applications.

[8]  Ana Belén Barragáns-Martínez,et al.  Developing a recommender system in a consumer electronic device , 2015, Expert Syst. Appl..

[9]  Patrice Perny,et al.  Preference-based Search and Machine Learning for Collaborative Filtering: the "Film-Conseil" Movie Recommender System , 2001 .

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

[11]  Miguel Ángel Rodríguez-García,et al.  Ontology-Based Music Recommender System , 2015, DCAI.

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

[13]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[14]  Jialie Shen,et al.  On Effective Location-Aware Music Recommendation , 2016, ACM Trans. Inf. Syst..

[15]  Lakshmi Tharun Ponnam,et al.  Movie recommender system using item based collaborative filtering technique , 2016, 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS).

[16]  Paul Martin Lester Digital Innovations for Mass Communications: Engaging the User , 2013 .

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

[18]  Logesh Ravi,et al.  A personalised movie recommendation system based on collaborative filtering , 2017, Int. J. High Perform. Comput. Netw..

[19]  Dmitri Botvich,et al.  Agent based middleware for private data mashup in IPTV recommender services , 2011, 2011 IEEE 16th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[20]  Denis Parra,et al.  Walk the talk: analyzing the relation between implicit and explicit feedback for preference elicitation , 2011, UMAP'11.

[21]  Jie Lu,et al.  A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services , 2015, IEEE Transactions on Fuzzy Systems.

[22]  Zafar Ali,et al.  Recommender Systems: Issues, Challenges, and Research Opportunities , 2016 .

[23]  Izak Benbasat,et al.  Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012 , 2014 .

[24]  R. Logesh,et al.  A personalised travel recommender system utilising social network profile and accurate GPS data , 2018 .

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

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

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

[28]  Hua Lin,et al.  A hybrid fuzzy-based personalized recommender system for telecom products/services , 2013, Inf. Sci..

[29]  Ronald R. Yager,et al.  Fuzzy logic methods in recommender systems , 2003, Fuzzy Sets Syst..

[30]  CARLOS A. GOMEZ-URIBE,et al.  The Netflix Recommender System , 2015, ACM Trans. Manag. Inf. Syst..

[31]  Manoj Kumar,et al.  A Movie Recommender System: MOVREC , 2015, International Journal of Computer Applications.

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

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

[34]  Enrique Herrera-Viedma,et al.  A Fuzzy Linguistic Recommender System to Advice Research Resources in University Digital Libraries , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[35]  S. Yamada,et al.  A movie recommender system based on inductive learning , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[36]  Logesh Ravi,et al.  A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users , 2016, Comput. Intell. Neurosci..