Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system.
Eyad KannoutMichal GrodzkiMarek GrzegorowskiPublished in: Comput. Sci. Inf. Syst. (2023)
Keyphrases
- cold start problem
- agglomerative clustering
- collaborative filtering
- fp growth
- recommender systems
- frequent patterns
- association rule mining
- data sparsity
- personalized recommendation
- cold start
- frequent itemsets
- mining frequent patterns
- frequent pattern mining
- matrix factorization
- association rules
- item based collaborative filtering
- mining association rules
- recommendation systems
- clustering algorithm
- fp tree
- mining algorithm
- k means
- tree structure
- user ratings
- itemsets
- minimum support
- user preferences
- apriori algorithm
- recommendation algorithms
- data clustering
- data structure
- data mining
- transfer learning
- user profiles
- pattern mining
- hierarchical clustering
- implicit feedback