เปรียบเทียบวิธี

	การวิเคราะห์เครือข่ายสองส่วน	การกรองแบบร่วมมือ (Collaborative Filtering)
สาขาวิชา	การวิเคราะห์เครือข่าย	การเรียนรู้ของเครื่อง
ตระกูล	Process / pipeline	Machine learning
ปีกำเนิด	1997	2001
ผู้ริเริ่ม	Borgatti & Everett (1997) formalised the two-mode network framework	GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization)
ประเภท	Graph-structural / relational analysis	Recommendation from user-item interactions
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	2
สรุป	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).