Salīdzināt metodes

	Divīziju tīklu analīze	Sadarbības filtrēšana
Nozare	Tīklu analīze	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	1997	2001
Autors	Borgatti & Everett (1997) formalised the two-mode network framework	GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization)
Tips	Graph-structural / relational analysis	Recommendation from user-item interactions
Pirmavots	Borgatti, S.P. & Everett, M.G. (1997). Network Analysis of 2-Mode Data. Social Networks, 19(3), 243-269.	Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 285–295.
Citi nosaukumi	two-mode network analysis, affiliation network analysis, İki Modlu Ağ Analizi (Bipartite Networks)	user-based collaborative filtering, item-based collaborative filtering, matrix factorization recommender, işbirlikçi filtreleme
Saistītās	5	2
Kopsavilkums	Bipartite network analysis, formalised by Borgatti and Everett in 1997, is a graph-structural method for studying networks in which nodes are divided into two disjoint sets — actors and events — and edges exist only between sets, never within them. It is the natural framework for author–paper, patient–disease, user–product, and any other affiliation data, and it extends one-mode network analysis by providing metrics and projection techniques tailored to the two-mode structure.	Collaborative filtering recommends items to a user by leveraging the preferences of many users — 'people who liked what you liked also liked this'. It learns from a sparse user-item interaction matrix, either by finding similar users or items (neighbourhood methods, formalized by Sarwar et al. in 2001) or by factorizing the matrix into latent user and item factors (matrix factorization, popularized by Koren et al. after the Netflix Prize).