# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 3 Proposed Methodology
Emerg. Top. Comput._**2013**, \(2\), 239-250. [CrossRef]
* (19) [PERSON]; [PERSON]; [PERSON] FISM: Factored item similarity models for top-N recommender systems. In Proceedings of the 19 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Chicago, IL, USA, 11-14 August 2013; pp. 659-667. [CrossRef]
* (20) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] From Zero-Shot Learning to Cold-Start Recommendation. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, 27 January-1 February 2019; Volume 33, pp. 4189-4196. [CrossRef]
* (21) [PERSON]; [PERSON]; [PERSON] Topic model-based recommender systems and their applications to cold-start problems. _Expert Syst. Appl._**2022**, _202_, 117129. [CrossRef]
* (22) [PERSON]; [PERSON] Simultaneous co-clustering and learning to address the cold start problem in recommender systems. _Knowl.-Based Syst._**2015**, _82_, 11-19. [CrossRef]
* (23) [PERSON]; [PERSON]; [PERSON]; [PERSON] Whom should I trust? the impact of key figures on cold start recommendations. In Proceedings of the 2008 ACM Symposium on Applied Computing, Association for Computing Machinery, Fortaleza, Ceara, Brazil, 16-20 March 2008; pp. 2014-2018. [CrossRef]
* (24) [PERSON]; [PERSON] Social network data to alleviate cold-start in recommender system: A systematic review. _Inf. Process. Manag._**2018**, _54_, 529-544. [CrossRef]
* (25) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. _Inf. Sci._**2020**, _536_, 156-170. [CrossRef]
* (26) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information. _IEEE Trans. Knowl. Data Eng._**2016**, _28_, 1147-1159. [CrossRef]
* (27) [PERSON]; [PERSON] Sparsity Regularization for Cold-Start Recommendation. _arXiv_**2022**. [CrossRef]
* (28) [PERSON]; [PERSON]; [PERSON] Learning preferences of new users in recommender systems: An information theoretic approach. _ACM SIGKDD Explor. Newsl._**2008**, _10_, 90-100. [CrossRef]
* (29) [PERSON]; [PERSON]; [PERSON]; [PERSON] Eigentaste: A Constant Time Collaborative Filtering Algorithm. _Inf. Retr._**2001**, \(4\), 133-151. [CrossRef]
* (30) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Application of Dimensionality Reduction in Recommender System--A Case Study_; Minnesota University Minneapolis Department of Computer Science: Minneapolis, MN, USA, 2000. * (31) [PERSON]; [PERSON]; [PERSON] Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. _J. ACM Trans. Inf. Syst._**2004**, _22_, 116-142. [CrossRef]
* (32) [PERSON]; [PERSON] Clustering approach for hybrid recommender system. In Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI 2003), Halifax, NS, Canada, 13-17 October 2003. [CrossRef]
* (33) [PERSON]; [PERSON]; [PERSON]; [PERSON] An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework. _Neurocomputing_**2016**, _191_, 388-397. [CrossRef]* _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON] Using Association Rules to Solve the Cold-Start Problem in Recommender Systems. In _Advances in Knowledge Discovery and Data Mining_; Springer: Berlin/Heidelberg, Germany, 2010. [CrossRef]
* [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON] An empirical study of a cross-level association rule mining approach to cold-start recommendations. _Knowl.-Based Syst._**2008**, _21_, 515-529. [CrossRef]
* _[PERSON] et al. (2002)_ [PERSON]; [PERSON]; [PERSON] Efficient Adaptive-Support Association Rule Mining for Recommender Systems. _Data Min. Knowl. Discov._**2002**, \(6\), 83-105.