Recommendation as Classification: Using Social and Content-Based Information in Recommendation

By C. Basu, H. Hirsh, William W. Cohen. 1998

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Citations in the corpus

Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “Such methods can make recommendations for new items but are limited in their performance since they cannot employ similarity between user preferences [5, 10, 11].”
Hybrid Systems for Personalized Recommendations (2003, background): “This is a way to expand the capabilities of a well-understood and well-tuned system, by adding new kinds of features into the mix [ 2 , 24].”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “Since the tastes are independent, a recommendation may be inferred from a set of participating clusters that best describe the active user [10, 74, 162].”
Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment (2005, background): “Basu et al., (2001) studied this issue in the context of assigning conference paper submissions to reviewing committee members.”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “For example, each user can be treated as a separate classification problem [Basu et al. 1998; Billsus and Pazzani 1998].”
E-Commerce Recommendation Applications (2004, methodology): “Basu et al. (1998) built a hybrid recommender system that mixes collaborative and content filtering using an induction-learning classifier.”
Recommender Systems (2012, background): “…their predictions [162,163]; (ii) add content-based characteristics to collaborative models [164–166]; (iii) add collaborative characteristics to content-based models [167] and (iv) develop a general unified model that integrate both content-based and collaborative characteristics [32,168–171].”
Content-Based Recommendation Systems (2007, background): “[4] add features to examples that indicate the identifiers of other users who like an item.”
A Survey of Collaborative Filtering Techniques (2009, methodology): “Model-based CF algorithms, such as Bayesian models, clustering models, and dependency networks, have been investigated to solve the shortcomings of memory-based CF algorithms [9, 71].”
Content-boosted collaborative filtering for improved recommendations (2002, background): “In another approach, Basu et al. (1998) treat recommending as a classification task.”
Predictive Statistical Models for User Modeling (2001, unknown): “Under the collaborative approach, Basu et al. (1998) used Ripper to learn a set of rules which predict whether a user will like or dislike a film, and Litman and Pan (2000) used Ripper to learn a set of rules that adapt the dialogue strategy used by a spoken dialogue system.”
Item-based top-N recommendation algorithms (2004, background): “Over the years, various approaches for building recommender systems have been developed that utilize either demographic, content, or historical information [Hill et al. 1995; Balabanovic and Shoham 1997; Basu et al. 1998; Shardanand and Maes 1995; Terveen et al. 1997; Konstan et al. 1997].”
Adaptive web search based on user profile constructed without any effort from users (2004, unknown): “クラスタリングモデルは,協調フィルタリングを分類問題として扱う [20], [21].”
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System (2015, methodology): “This task is traditionally answered by using heuristic methods [18] or classification algorithms, such us: rule induction, nearest neighbors methods, Rocchio‟s algorithm, linear classifiers and probabilistic methods [13] .”
Data Mining Methods for Recommender Systems (2015, methodology): “[10], for instance, use this approach by analyzing which of the items predicted in the top quartile of the rating scale were actually”
Trust and nuanced profile similarity in online social networks (2009, methodology): “1998; Basu et al. 1998]. Coclustering methods that cluster both users and items have been shown to be effective and computationally efficient [George and Merugu 2005]. Bayesian network models [Breese et al. 1998; Chien and George 1999] build a probabilistic model of the users’ preferences, generatin”
Latent semantic models for collaborative filtering (2004, methodology): “Model-based techniques have been investigated before, most notably Bayesian and non-Bayesian clustering techniques [Breese et al. 1998; Ungar and Foster 1998; Basu et al. 1998; Chien and George 1999], Bayesian networks [Breese et al. 1998], and dependency networks [Heckerman et al. 2000].”
Hybrid Web Recommender Systems (2007, methodology): “Basu, Hirsh and Cohen [3] used the inductive rule learner Ripper [16] to learn content-based rules about user's likes and dislikes.”
Understanding and improving automated collaborative filtering systems (2000, methodology): “For the evaluation of ACF systems, they have been used by Billsus [10] and Basu [16].”