Anna Zagranovskaia
ABSTRACT
The purpose of the study is to analyze the existing methods of building hybrid recommender systems and develop the most accurate and adequate movie recommender system of that type. The paper addresses basic algorithms for designing recommender systems (content-based, collaborative filtering, hybrids), and corresponding similarity metrics (cosine distance, Pearson correlation coefficient). The interest in hybrid systems is due to the fact that combining different modelling methods can improve not only the accuracy of rating predictions but also other characteristics of a recommender system, such as novelty, serendipity, diversity. In the course of the study, three approaches were used: content-based, collaborative and hybrid ones. As a result, two best models were chosen based on the first two approaches, with reference to which a hybrid model was built, whose quality was evaluated through a survey of potential users of the system. This made it possible to cover a whole range of evaluation criteria for recommender systems and understand corresponding user requirements. Trust to the system turned out to be the most important one. In other words, it means the extent to which the user is confident that he or she will actually like the recommendations suggested. Recommender systems were tested using open source data of the MovieLens dataset. This makes it possible to verify the results and improve the models suggested.
- F. Ricci, L. Rokach, B. Shapira and P.B. Kantor. 2015. Recommender Systems Handbook. New York.: Springer, 1003. Google Scholar
Digital Library
- J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez. 2013. Recommender systems survey. Knowledge-Based Systems 46 (July), 109-132. Google ScholarDigital Library
- Bagher Rahimpour Cami, Hamid Hassanpour and Hoda Mashayekhi. 2019. User preferences modeling using dirichlet process mixture model for a content-based recommender system. Knowledge-Based Systems 163 (1 January), 644-655.Google Scholar
- Alan Eckhardt. 2012. Similarity of users’ (content-based) preference models for Collaborative filtering in few ratings scenario. Expert Systems with Applications 39, 14 (15 October), 11511-11516. Google ScholarDigital Library
- F.O. Isinkaye, Y.O. Folajimi and B.A. Ojokoh. 2015. Recommendation systems: Principles, methods and Evaluation. Egyptian Informatics Journal 16, 3 (November), 261-273.Google Scholar
- Gediminas Adomavicius and Alexander Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions On Knowledge And Data Engineering 17, 6 (June), 734-748. Google ScholarDigital Library
- Pinata Winoto and Tiffany Y. Tang. 2010. The role of user mood in movie recommendations. Expert Systems with Applications 37, 6086-6092. Google ScholarDigital Library
- Bidyut Kr. Patra, Raimo Launonen, Ville Ollikainen and Sukumar Nandi. 2015. A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Systems 82 (July), 163-177. Google ScholarDigital Library
- M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes and M. Sartin. 1999. Combining Content-Based and Collaborative Filters in an Online Newspaper. M.: Berkeley, CA. SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation.Google Scholar
- Machine Learning for Recommender systems-Part 1 (algorithms, evaluation and cold start) [Electronic resource]. Medium. Available at: https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed (accessed: 15.04.2019).Google Scholar
- R. Burke. 2002. User Model User-Adap Inter 12, 331. DOI: https://doi.org/10.1023/A:1021240730564. Google ScholarDigital Library
- D. Billsus and M.J. Pazzani. 1999. A hybrid user model for news story classification. Banff, Canada. Springer-Verlag, New York. Proceedings of the seventh international conference on user modeling, 99-108. Google ScholarDigital Library
- B. Smyth and P. Cotter. 2000. A personalised TV listings service for the digital TV age. Knowl. Based Syst. 13, 53-59. Google ScholarDigital Library
- C. Basu, H. Hirsh and W. Cohen. Recommendation as Classification: Using Social and Content-Based Information in Recommendation. Madison, WI. Proceedings of the 15th National Conference on Artificial Intelligence, 714-720. Google ScholarDigital Library
- R.J. Mooney and L. Roy. 1999. Content-Based Book Recommending Using Learning for Text Categorization. Berkeley, CA. SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation. Google ScholarDigital Library
- B.M. Sarwar, J.A. Konstan, A. Borchers, J. Herlocker, B. Miller and J. Riedl. 1998. Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. Seattle, WA. Proceedings of the ACM Conference on Computer Supported Cooperative Work, 345-354. Google ScholarDigital Library
- M.K. Condliff, D.D. Lewis, D. Madigan and C. Posse. 1999. Bayesian Mixed-Effects Models for Recommender Systems. Berkeley, CA. SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation.Google Scholar
- Grouplens, Datasets, MovieLens, Recommended for education and development small dataset [Electronic resource]. Available at: https://grouplens.org/datasets/movielens/ (accessed: 30.08.2018).Google Scholar
- Straw Poll. The place to create instant, real-time polls for free [Electronic resource]. Available at: https://www.strawpoll.me (accessed: 02.06.2019).Google Scholar
Index Terms
- Designing Hybrid Recommender Systems
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingCollaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the ...
A unified approach to building hybrid recommender systems
RecSys '09: Proceedings of the third ACM conference on Recommender systemsContent-based recommendation systems can provide recommendations for "cold-start" items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering ...
Comments