Abstract Existing approaches for Recommendation Systems (RS) are mainly based on users’ past knowledge and the more popular techniques such as the neighborhood models focus on finding similar users in making recommendations. The cold start problem is due to inaccurate recommendations given to new users because of lack of past data related to those users. To deal with such cases where prior information on the new user is not available, this paper proposes a normalization technique to model user involvement for cold start problem or user likings based on the details of items used in the neighborhood models. The proposed normalization technique was evaluated using two datasets namely MovieLens and GroupLens. The results showed that the proposed technique is able to improve the accuracy of the neighborhood model, which in turn increases the accuracy of an RS.

References

[1] Al-Hadi I., Sharef N., Sulaiman M., and Mustapha N., “Ensemble Divide and Conquer Approach to Solve the Rating Scores’ Deviation in Recommendation System,” Journal of Computer Science, vol. 12, no. 6, pp. 265-275, 2016.

[2] Al-Hadi I., Sharef N., Sulaiman M., and Mustapha N., “Review of the Temporal Recommendation System with Matrix Factorization,” International Journal of Innovative Computing, Information and Control, vol. 13, no. 5, pp. 1579-1594, 2017.

[3] Anand D. and Bharadwaj K., “Utilizing Various Sparsity Measures for Enhancing Accuracy of Collaborative Recommender Systems Based on Local and Global Similarities,” Expert Systems with Applications, vol. 38, no. 5, pp. 1501-1509, 2011.

[4] Bell R. and Koren Y., “Lessons from the Netflix Prize Challenge,” ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 75-79, 2007.

[5] Bell R. and Koren Y., “Improved Neighborhood- Based Collaborative Filtering,” in Preseedings of KDD Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, pp. 7-14, 2007.

[6] Bobadilla J., Ortega F., Hernando A., and Gutiérrez A., “Recommender Systems Survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.

[7] Burke R., “Hybrid recommender Systems: Survey and Experiments,” User Modeling and User- Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.

[8] Burke R., The Adaptive Web, Springer, 2007.

[9] Chandak M., Girase S., and Mukhopadhyay D., “Introducing Hybrid Technique for Optimization of Book Recommender System,” Procedia Computer Science, vol. 45, pp. 23-31, 2015.

[10] Chen J., Liu Y., and Li D., “Dynamic Group Recommendation with Modified Collaborative Filtering and Temporal Factor,” The International Arab Journal of Information Technology, vol. 13, no. 2, pp. 294-301, 2016.

[11] Chen J., Wang H., and Yan Z., “Evolutionary Heterogeneous Clustering for Rating Prediction Based on User Collaborative Filtering,” Swarm and Evolutionary Computation, vol. 38, pp. 35- 41, 2017.

[12] Frasincar F., IJntema W., Goossen F., and Hogenboom F., Business Intelligence Applications and the Web, IGI Global, 2012.

[13] Ghazanfar M. and Prügel-Bennett A., “Leveraging Clustering Approaches to Solve the Gray-Sheep Users Problem in Recommender Systems,” Expert Systems with Applications, vol. 41, no. 7, pp. 3261-3275, 2014.

[14] Koren Y., Bell R., and Volinsky C., “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30-37, 2009.

[15] Leung C., Chan S., and Chung F., “An Empirical Study of A Cross-Level Association Rule Mining Approach to Cold-Start Recommendations,” Knowledge-Based Systems, vol. 21, no. 7, pp. 515-529, 2008.

[16] Luo X., Xia Y., and Zhu Q., “Incremental Collaborative Filtering Recommender Based on Regularized Matrix Factorization,” Knowledge- Based Systems, vol. 27, pp. 271-280, 2012.

[17] Luo X., XiaY., and Zhu Q., “Applying the Learning Rate Adaptation to the Matrix Factorization Based Collaborative Filtering,” Knowledge-Based Systems, vol. 37, pp. 154-164, 2013.

[18] Montaner M., López B., and De La Rosa J., “A Taxonomy of Recommender Agents on the Internet,” Artificial Intelligence Review, vol. 19, no. 4, pp. 285-330, 2003.

[19] Resnick P., Iacovou N., Suchak M., Bergstrom P., and Riedl J., “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” in Proceedings of ACM Conference on Computer Supported Cooperative Work, Chapel Hill, pp. 175-186, 1994.

[20] Schafer J., Konstan J., and Riedl J., “Recommender Systems in E-Commerce,” in Proceedings of the 1st ACM Conference on Electronic Commerce, Denver, pp. 185-166, 1999.

[21] Schiaffino S. and Amandi A., “Building an Expert Travel Agent as A Software Agent,” Expert Systems with Applications, vol. 36, no. 2, pp. 1291-1299, 2009.

[22] Shardanand U. and Maes P., “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” in Proceedings of SIGCHI Conference on Human Factors in Computing Systems, Denver, pp. 210-217, 1995.

[23] Thong N., “Intuitionistic Fuzzy Recommender Systems: An Effective Tool for Medical 290 The International Arab Journal of Information Technology, Vol. 17, No. 3, May 2020 Diagnosis,” Knowledge-Based Syst, vol. 74, pp. 133-150, 2015.

[24] Toledo R., Mota Y., and Martínez L., “Correcting Noisy Ratings in Collaborative Recommender Systems,” Knowledge-Based Systems, vol. 76, pp. 96-108, 2015.

[25] Vizine Pereira A. and Hruschka E., “Simultaneous Co-Clustering and Learning to Address the Cold Start Problem in Recommender Systems,” Knowledge-Based Systems, vol. 82, pp. 11-19, 2015.

[26] Zahid A. and Sharef M., “Normalization and Matrix Factoization-based Methods for Recommender System,” in Proceedings of 3rd International conference on Computer Science and Computational Mathematics, Langkawi, pp. 378-383, 2014.

[27] Zhuang L., Jing F., and Zhu X., “Movie Review Mining and Summarization,” in Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 43- 50, Arlington, 2006. Aafaq Zahid is a Master Degree holder in Artificial Intelligence, and has been developing complex mission critical applications for the past 8 years. With his ambition in Artificial intelligence he combined AI in different other areas of computer science. His recent works include combining AI with GPS to achieve the best results for car tracking and vehicle malfunctions predictions. He currently holds position of Head of R&D Department in Azure Innovations, Malaysia. Nurfadhlina Mohd Sharef is an Associate Professor at the Department of Computer Science and currently Head of Intelligent Computing Research Group at the Faculty of Computer Science and Information Technology, University of Putra, Malaysia. Her research interests are in the areas of Intelligent Computing and she has experience in both academic and consultancy projects involving data science and analytics, text mining, semantic web, and recommendations systems. She also has experience in building data mining solutions for various domains such as economic, medical, logistics. Among her recent projects are the development of adaptive method for the translation of natural language question to semantic query language, development of temporal based recommender system using bacterial foraging optimization algorithm, development of an adaptive method for feature selection and multi- objective optimization for breast cancer recurrence prediction, and development of deep learning model for multi-class classification of tweets. She was also engaged in several consultation projects such as the design of the online logistics aggregation web-based and mobile-based service, the design of the fuzzy aggregation based data analytics for security threat profiling and database integration from heterogeneous resources. Aida Mustapha received the B.Sc. degree in Computer Science from Michigan Technological University and the M.IT degree in Computer Science from UKM, Malaysia in 1998 and 2004, respectively. She received her Ph.D. in Artificial Intelligence focusing on dialogue systems. She is currently an active researcher in the area of Computational Linguistics, Soft Computing, Data Mining, and Agent-based Systems.