A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

By Hyung Jun Ahn. 2008

725 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, neutral): “similarity, and Spearman’s rank correlation [1].”
A collaborative filtering approach to mitigate the new user cold start problem (2012, methodology): “There are so far two research papers which deal with the coldstart problem through the users’ ratings information: Hyung [22] presents a heuristic similarity measure named PIP, that outperforms the traditional statistical similarity measures (Pearson correlation, cosine, etc.); and Heung et al. [16]”
Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “Unlike the traditional cold-start problem in the recommender system [21, 1], where the users or items come to the system with no ratings, a user is new only for the unfamiliar area in terms of the new city problem in location-based recommendation.”
Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014, background): “Copyright 2014 by the author(s). mographic information) to improve predictive performance (Claypool et al., 1999; Park et al., 2006; Ahn, 2008; Park & Chu, 2009).”
A new user similarity model to improve the accuracy of collaborative filtering (2014, methodology): “The ACOS also has this problem.”
Recommender systems survey (2013, background): “Additionally, a group of metrics was specifically designed to adequately function in cold-start situations [6,36].”
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks (2010, background): “Generally, collaborative systems report a better performance than content-based approaches, but their success relies on the presence of a sufficient number of user ratings [3,5,6,4,7].”
Alors: An algorithm recommender system (2017, methodology): “When it is the case, CF successfully handles the so-called cold-start task (Ahn, 2008; Park and Chu, 2009; Liu et al., 2014): emitting recommendations for a brand new user or selecting algorithms for a brand new problem instance.”
Predicting novel herbivore–plant interactions (2013, methodology): “…predict customer preference for products are also built to make predictions based on similarity, with similar products defi ned as those that receive similar ratings from diff erent users, or with similar users defi ned as those that give similar ratings to products (Schein et al. 2002, Ahn 2008).”