A heuristic concept construction approach to collaborative recommendation

Formal concept analysis was first used in collaborative filtering for over one decade. Popular approaches are based on superconcept-subconcept relationship or boolean matrix factorization. In this paper, we design a heuristic approach to construct a set of approximately strong concepts for recommendation. Here strong refers to not only big intent to ensure the similarity among users, but also big extent to ensure the stability of user groups. First, we use the intent threshold as the constraint and the area as the optimization objective to obtain approximately strong concepts. Second, we generate pre recommendations based on the local popularity of the items implied by each concept. Finally, we determine the actual recommendation according to the number of times the item is pre-recommended to the user. Experiments have been undertaken on five popular datasets with different versions. Results show that our algorithm has lower runtime and/or higher recommendation quality compared with approaches based on concept lattice, matrix factorization, k-nearest neighbors, item-based collaborative filtering and boolean matrix factorization. (C)& nbsp;2022 Elsevier Inc. All rights reserved.