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.
|