User and item profile expansion for dealing with cold start problem

[1]  Nick Bassiliades,et al.  Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems , 2018, Expert Syst. Appl..

[2]  Xuegang Mao,et al.  Object-based forest gaps classification using airborne LiDAR data , 2018, Journal of Forestry Research.

[3]  Hui Xiong,et al.  Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Hamid Parvin,et al.  Dynamic protein–protein interaction networks construction using firefly algorithm , 2017, Pattern Analysis and Applications.

[5]  Mohammad Yahya H. Al-Shamri,et al.  User profiling approaches for demographic recommender systems , 2016, Knowl. Based Syst..

[6]  En Wang,et al.  Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm , 2018, IEEE Transactions on Multimedia.

[7]  Victor Carneiro,et al.  Using profile expansion techniques to alleviate the new user problem , 2013, Inf. Process. Manag..

[8]  Hamid Parvin,et al.  Explicit memory based ABC with a clustering strategy for updating and retrieval of memory in dynamic environments , 2018, Applied Intelligence.

[9]  Roliana Ibrahim,et al.  Facebook Interactions Utilization for Addressing Recommender Systems Cold Start Problem across System Domain , 2018 .

[10]  Akram Salah,et al.  Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System , 2013 .

[11]  David Sánchez,et al.  Turist@: Agent-based personalised recommendation of tourist activities , 2012, Expert Syst. Appl..

[12]  Ahmet Arslan,et al.  A collaborative filtering method based on artificial immune network , 2009, Expert Syst. Appl..

[13]  Ning Zhang,et al.  An improved collaborative filtering method based on similarity , 2018, PloS one.

[14]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[15]  Hamid Parvin,et al.  Diversity based cluster weighting in cluster ensemble: an information theory approach , 2019, Artificial Intelligence Review.

[16]  Hamid Parvin,et al.  Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters , 2019, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[17]  Hamid Parvin,et al.  A linear unsupervised transfer learning by preservation of cluster-and-neighborhood data organization , 2018, Pattern Analysis and Applications.

[18]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[19]  Hamid Parvin,et al.  Clustering ensemble selection considering quality and diversity , 2015, Artificial Intelligence Review.

[20]  Hamid Parvin,et al.  Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification , 2018, Neurocomputing.

[21]  Hamid Parvin,et al.  A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters , 2019, Applied intelligence (Boston).

[22]  Luis Martínez,et al.  Fuzzy Tools in Recommender Systems: A Survey , 2017, Int. J. Comput. Intell. Syst..

[23]  Antonio Moreno,et al.  Intelligent tourism recommender systems: A survey , 2014, Expert Syst. Appl..

[24]  Xiao Ma,et al.  EARS: Emotion-aware recommender system based on hybrid information fusion , 2019, Inf. Fusion.

[25]  Hamid Parvin,et al.  Elite fuzzy clustering ensemble based on clustering diversity and quality measures , 2018, Applied Intelligence.

[26]  Aviezri S. Fraenkel,et al.  Local Feedback in Full-Text Retrieval Systems , 1977, JACM.

[27]  Iraklis Varlamis,et al.  Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions , 2018, Future Gener. Comput. Syst..

[28]  Hamid Parvin,et al.  A comprehensive study of clustering ensemble weighting based on cluster quality and diversity , 2017, Pattern Analysis and Applications.

[29]  Behzad Soleimani Neysiani,et al.  Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm , 2019, International Journal of Information Technology and Computer Science.

[30]  Hamid Parvin,et al.  An innovative linear unsupervised space adjustment by keeping low-level spatial data structure , 2018, Knowledge and Information Systems.

[31]  S. Nejatian,et al.  Gene Regulatory Elements Extraction in Breast Cancer by Hi-C Data Using a Meta-Heuristic Method , 2019, Russian Journal of Genetics.

[32]  Ming Zhu,et al.  Ontology-based Top-N Recommendations on New Items with Matrix Factorization , 2014, J. Softw..