A collaborative filtering algorithm and evaluation metric that accurately model the user experience

By Matthew R. McLaughlin, Jonathan L. Herlocker. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

385 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/c6fd79673d2fd5c82aeb9ee1aea10bddc90887d8

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In Computer Science Applications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Collaborative Filtering Recommender Systems (2011, background): “In offline analysis of available movie ratings data, Herlocker et al. found k = 20 to be a good value; values in the 20–50 range are a reasonable starting point in many domains.”
WSRec: A Collaborative Filtering Based Web Service Recommender System (2009, background): “PCC often overestimates the similarities of service users who are actually not similar but happen to have similar QoS experience on a few co-invoked Web services [11].”
Challenging the Long Tail Recommendation (2012, methodology): “Similarly, the recommendation results from the classic collaborative ﬁlter-ing(e.g. [12, 20]) are always local popular and obvious.”
Multi-Domain Collaborative Filtering (2010, neutral): “Some representative methods are [10, 23, 16].”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, methodology): “It can overestimate the similarity of two users that are not similar, but happen to have a few services with very similar QoS records [21].”
Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang (2011, background): “A popular approach to CF is based on the principle of locality of dependencies, which assumes that the interaction between user i and item j can be restored solely upon the observations of neighboring users or items [24] [17].”
Collaborative Filtering Recommender Systems (2007, background): “Common rank accuracy metrics include precision [41, 52] and half-life utility”
Data Mining Methods for Recommender Systems (2015, neutral): “McLaughlin and Herlocker [50] propose”
A Survey of Collaborative Filtering Techniques (2009, background): “Some variations of item-based and user-based Pearson correlations can be found in [59].”
Collaborative competitive filtering: learning recommender using context of user choice (2011, methodology): “Then, an unseen response between user u and item i is approximated based on the responses of neighboring users or items [22, 19], for example, by simply averaging the neighboring responses with similarities as weights.”
Improving recommendation lists through topic diversification (2005, neutral): “several blatant flaws in existing systems [17].”