A Comprehensive Survey of Neighborhood-based Recommendation Methods

By Christian Desrosiers, George Karypis. Recommender Systems Handbook, 2011

988 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/db96d6dd5e70abb88b1f25c4d66942e3879f549d

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In Computer Networks and Communications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “Collaborative Filtering (CF) approaches are usually grouped in two main classes: neighborhood- and model-based [12].”
Recommender Systems (2016, positive): “A discussion of the use of voting techniques in nearest neighbor methods may be found in [183].”
Diversity, Serendipity, Novelty, and Coverage (2016, methodology): “…2010] (LTR), a PureSVD [Cremonesi et al. 2010] matrix factorization algorithm implemented using the sparsesvd library 8 (MF), and two k-nearest-neighbor algorithms—a user-based collaborative filtering method (UB) and an item-based collaborative filtering method (IB) [Desrosiers and Karypis 2011].”
Hierarchical Gating Networks for Sequential Recommendation (2019, background): “The relation between two single items is an important factor to model in the recommendation task and has been widely studied in many years [17, 25], e.”
Efficient top-n recommendation for very large scale binary rated datasets (2013, background): “Thus, the challenge focused on the ordering of the songs on the basis of the relevance for a given user (top-N recommendation), and this makes the particular problem different from the more classical problem of predicting rates a user will give to unseen items [6, 13].”
Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019, background): “ir preferences [22]. The ﬁeld subsequently evolved to use collaborative ﬁltering, where recommendations are based on past user behaviors, such as prior ratings given to products. Neighborhood methods [21] that provide recommendations by grouping users and products together and latent factor methods ”
Applying Differential Privacy to Matrix Factorization (2015, methodology): “In this experiment we compare the results of the privacypreserving MF approach to two other privacy preserving CF algorithms: a private version of the GE baseline and private k-Nearest Neighbors (kNN) algorithm [5].”
A survey of active learning in collaborative filtering recommender systems (2016, background): “It recommends items that the target user has not yet considered but will likely enjoy [34,35].”
Transfer Learning for Semisupervised Collaborative Recommendation (2016, methodology): “The advantage of ICF is its simplicity and good interpretation, which are usually preferred by practitioners concerning convenient code debugging, algorithm deployment, result interpretation, and system maintenance [Desrosiers and Karypis 2011].”