Browse > Article

Optimal Diversity of Recommendation List for Recommender Systems based on the Users' Desire Diversity  

Mehrjoo, Saeed (Department of Computer Science and Engineering, Dariun Branch, Islamic Azad University)
Mehrjoo, Mehrdad (Department of Electrical and Computer Engineering, University of Manitoba)
Hajipour, Farahnaz (Biomedical Engineering Program, University of Manitoba)
Publication Information
Journal of Information Science Theory and Practice / v.7, no.3, 2019 , pp. 31-39 More about this Journal
Nowadays, recommender systems suggest lists of items to users considering not only accuracy but also diversity and novelty. However, suggesting the most diverse list of items to all users is not always acceptable, since different users prefer and/or tolerate different degree of diversity. Hence suggesting a personalized list with a diversity degree considering each user preference would improve the efficiency of recommender systems. The main contribution and novelty of this study is to tune the diversity degree of the recommendation list based on the users' variety-seeking feature, which ultimately leads to users' satisfaction. The proposed approach considers the similarity of users' desire diversity as a new parameter in addition to the usual similarity of users in the state-of-the-art collaborative filtering algorithm. Experimental results show that the proposed approach improves the personal diversity criterion comparing to the closest method in the literature, without decreasing accuracy.
accuracy; collaborative filtering; personal diversity; recommender system;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37-51.   DOI
2 Bennett, J., & Lanning, S. (2007). The Netflix prize. Paper presented at the KDD Cup and Workshop, San Jose, CA, USA.
3 Castells, P., Hurley, N. J., & Vargas, S. (2015). Novelty and diversity in recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 881-918). New York: Springer.
4 Di Noia, T., Ostuni, V. C., Rosati, J., Tomeo, P., & Di Sciascio, E. (2014). An analysis of users' propensity toward diversity in recommendations. Paper presented at the 8th ACM Conference on Recommender Systems, Foster City, CA, USA.
5 Eskandanian, F., Mobasher, B., & Burke, R. (2017). A clustering approach for personalizing diversity in collaborative recommender systems. Paper presented at the 25th Conference on User Modeling, Adaptation and Personalization, Bratislava, Slovakia.
6 Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., & Reiterer, S. (2013). Toward the next generation of recommender systems: Applications and research challenges. In G. A. Tsihrintzis, M. V. Lakhmi, & C. Jain (Eds.), Multimedia services in intelligent environments (pp. 81-98). New York: Springer.
7 Harper, F. M., & Konstan, J. A. (2016). The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems, 5(4), 19.
8 Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42, 30-37.   DOI
9 Martinez-Cruz, C., Porcel, C., Bernabe-Moreno, J., & Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311, 102-118.   DOI
10 McNee, S. M., Riedl, J., & Konstan, J. A. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. Paper presented at the CHI'06 Extended Abstracts on Human Factors in Computing Systems, Montreal, Canada.
11 Medina-Moreira, J., Apolinario, O., Luna-Aveiga, H., Lagos-Ortiz, K., Paredes-Valverde, M. A., & Valencia-Garcia, R. (2017). A collaborative filtering based recommender system for disease self-management. Paper presented at the International Conference on Technologies and Innovation, Guayaquil, Ecuador.
12 Nguyen, T. T., Harper, F. M., Terveen, L., & Konstan, J. A. (2018). User personality and user satisfaction with recommender systems. Information Systems Frontiers, 20(6), 1173-1189.   DOI
13 Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507-520.   DOI
14 Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 1-35). New York: Springer.
15 Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Paper presented at the 10th International Conference on World Wide Web, Hong Kong.
16 Tkalcic, M., Kunaver, M., Tasic, J., & Kosir, A. (2009). Personality based user similarity measure for a collaborative recommender system. Paper presented at the 5th Workshop on Emotion in Human-Computer Interaction-Real World Challenges, Cambridge, UK.
17 Vargas, S., & Castells, P. (2013). Exploiting the diversity of user preferences for recommendation. Paper presented at the 10th Conference on Open Research Areas in Information Retrieval, Lisbon, Portugal.
18 Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010). Solving the apparent diversityaccuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10), 4511-4515.   DOI
19 Wu, W., Chen, L., & He, L. (2013). Using personality to adjust diversity in recommender systems. Paper presented at the 24th ACM Conference on Hypertext and Social Media, Paris, France.
20 Zhao, Z.-D., & Shang, M.-S. (2010). User-based collaborativefiltering recommendation algorithms on hadoop. Paper presented at 2010 Third International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand.