Getting to know you: learning new user preferences in recommender systems

By Al Mamunur Rashid, I. Albert, Dan Cosley, Shyong K. Lam, Sean M. McNee, J. Konstan, John Riedl. 2002

677 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Management Science and Operations Research

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In Computer Science Applications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Learning Algorithms for Active Learning (2017, methodology): “The Popular-Entropy policy, adapted from the cold-start work of Rashid et al. (2002), a priori scores each item in the support set according to the logarithm of its popularity multiplied by the entropy of the item’s ratings measured across users.”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, background): “force all new users to answer a few key questions [134].”
Collaborative Filtering Recommender Systems (2011, methodology): “[117] used the popularity of items, asking the user to rate items that were both popular and had high information value.”
A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “The new user problem [42,43,46] is among the great difficulties faced by the RS in operation.”
Learning multiple-question decision trees for cold-start recommendation (2013, background): “Copyright 2013 ACM 978-1-4503-1869-3/13/02 ...$15.00.”
Learning preferences of new users in recommender systems: an information theoretic approach (2008, methodology): “Note that we do not experiment with basic Entropy here, rather directly apply the learning from [23].”
Adaptive bootstrapping of recommender systems using decision trees (2011, methodology): “A comprehensive study was conducted by the GroupLens team [16], and was later enhanced [17].”
Functional matrix factorizations for cold-start recommendation (2011, methodology): “…argued that an eﬀective model for designing the interview process should ask the interview questions adaptively, taking into account the user’s responses when asking the next question in the interview process, and decision trees have been found to work especially well for this purpose [7,19,20].”
Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014, methodology): “Model-free strategies have been proposed for active data collection (Rashid et al., 2002; 2008), where empirical statistics of the data such as item popularity or rating entropy are used to select items.”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “These techniques use strategies that are based on item popularity, item entropy, user personalization, and combinations of the above [83], [109].”
On bootstrapping recommender systems (2010, methodology): “Thus, a reasonable strategy is to ask the user about the most popular items, i.e., those that have received the most ratings in the past [8].”
A Survey on Conversational Recommender Systems (2020, background): “Possible strategies, for example, include the selection of popular items in the cold-start phase, items that are diverse in terms of their past ratings or attributes, or itemsets that represent a balance of popularity and diversity [17, 96, 114].”
A survey of active learning in collaborative filtering recommender systems (2016, background): “• Popularity [20,60]: selects themost popular items, i.”
On the recommending of citations for research papers (2002, result): “We have observed this problem in earlier research in which the Item-Item algorithm tended to recommend movies that were very closely related to previously seen movies, resulting in too narrow a view of a user’s tastes [14].”
Recommendation Systems in Software Engineering (2014, background): “Natural language can contain important information about related bug severity [37], bug reports [58, 64], affected code [34, 55], etc.”
Recommendation systems: Principles, methods and evaluation (2015, background): “Recommender system is defined as a decision making strategy for users under complex information environments [6].”