APA 6th Edition Nadimi-Shahraki, M. i Bahadorpour, M. (2014). Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique. Journal of computing and information technology, 22 (2), 105-113. https://doi.org/10.2498/cit.1002223
MLA 8th Edition Nadimi-Shahraki, Mohammad-Hossein i Mozhde Bahadorpour. "Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique." Journal of computing and information technology, vol. 22, br. 2, 2014, str. 105-113. https://doi.org/10.2498/cit.1002223. Citirano 16.01.2021.
Chicago 17th Edition Nadimi-Shahraki, Mohammad-Hossein i Mozhde Bahadorpour. "Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique." Journal of computing and information technology 22, br. 2 (2014): 105-113. https://doi.org/10.2498/cit.1002223
Harvard Nadimi-Shahraki, M., i Bahadorpour, M. (2014). 'Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique', Journal of computing and information technology, 22(2), str. 105-113. https://doi.org/10.2498/cit.1002223
Vancouver Nadimi-Shahraki M, Bahadorpour M. Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique. Journal of computing and information technology [Internet]. 2014 [pristupljeno 16.01.2021.];22(2):105-113. https://doi.org/10.2498/cit.1002223
IEEE M. Nadimi-Shahraki i M. Bahadorpour, "Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique", Journal of computing and information technology, vol.22, br. 2, str. 105-113, 2014. [Online]. https://doi.org/10.2498/cit.1002223
Sažetak To develop a recommender system, the collaborative filtering is the best known approach, which considers the ratings of users who have similar rating profiles or rating patterns. Consistently, it is able to compute the similarity of users when there are enough ratings expressed by users. Therefore, a major challenge of the collaborative filtering approach can be how to make recommendations for a new user, that is called cold-start user problem. To solve this problem, there have been proposed a few efficient methods based on ask-to-rate technique in which the profile of a new user is made by integrating information gained from a quick interview. This paper is a review of these proposed methods and how to use the ask-to-rate technique. Consequently, they are categorized into non-adaptive and adaptive methods. Then, each category is analyzed and their methods are compared.