Collaborative filtering analyzes user preferences for items (eg, books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback …
Recommendation datasets are prone to selection biases due to self-selection behavior of users and item selection process of systems. This makes explicitly combating selection …
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A …
C Ma, C Zhang - Advances in Neural Information …, 2021 - proceedings.neurips.cc
Real-world datasets often have missing values associated with complex generative processes, where the cause of the missingness may not be fully observed. This is known as …
D Liang, L Charlin, DM Blei - … to Application, Workshop at UAI. AUAI, 2016 - its.caltech.edu
We develop a causal inference approach to recommender systems. Observational recommendation data contains two sources of information: which items each user decided to …
A Elbadrawy, G Karypis - Proceedings of the 10th ACM conference on …, 2016 - dl.acm.org
Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based …
MC Yuen, I King, KS Leung - … of the first international workshop on …, 2012 - dl.acm.org
In crowdsourcing systems, tasks are distributed to networked people to complete such that a company's production cost can be greatly reduced. Obviously, it is not efficient that the …
M Wang, M Gong, X Zheng… - Advances in neural …, 2018 - proceedings.neurips.cc
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are\emph {missing …
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar …