Unifying collaborative and content-based filtering

By Justin Basilico, Thomas Hofmann. 2004

429 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (2012, methodology): “These model-based approaches, especially latent-factor models based on matrix factorization (Koren et al., 2009; Salakhutdinov & Mnih, 2008), have shown promise in better rating prediction since they eﬃciently incorporate user interests into the model.”
Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, background): “Hybrid recommendation systems seek the best of both worlds, by leveraging both item content and user-item ratings [5, 10, 12, 13, 14].”
Kernel methods in machine learning (2007, neutral): “Similar models were also studied by Basilico and Hofmann [13].”
Bundle Methods for Regularized Risk Minimization (2010, background): “They are used to address the issue of evaluating rankers, search engines or recommender sytems (Voorhees, 2001; Jarvelin and Kekalainen, 2002; Breese et al., 1998; Basilico and Hofmann, 2004).”
Support Vector Ordinal Regression (2007, background): “Pearson correlation coefficient (a particular dot product between normalized rating vectors) is the most popular correlation measure in collaborative filtering (Basilico and Hofmann, 2004).”
Multiple domain user personalization (2011, background): “To prevent information overload, many collaborative filtering and recommendation systems [1, 19, 8, 3, 2] have been developed to filter the information stream and display only information the user may be interested in.”
Functional matrix factorizations for cold-start recommendation (2011, background): “The content features can be used to capture the similarities between users and items and thus reduce the amount of data required to make accurate predictions [2,3,10,15,23,25].”
Comparison of collaborative filtering algorithms (2011, methodology): “They are the so called hybrids that combine content-based methods with collaborative filtering [Melville et al. 2001; Basilico and Hofmann 2004].”
Ratings meet reviews, a combined approach to recommend (2014, methodology): “Due to the advantage of taking the review text into consideration, there are a few efforts [2, 4, 16, 17] explored the combination of content-based filtering and collaborative filtering.”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…across speakers (Yang and Eisenstein, 2017), though attempts at creating personalized language technologies exist in information retrieval (Shen et al., 2005), recommender systems (Basilico and Hofmann, 2004), machine translation (Mirkin and Meunier, 2015), and language modeling (Federico, 1996).”
Ordinal Boltzmann Machines for Collaborative Filtering (2009, methodology): “Machine learning (Billsus and Pazzani, 1998; Basu et al., 1998; Basilico and Hofmann, 2004) has also been successfully applied to the collaborative ltering problem.”
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks (2010, methodology): “Some examples are [30,41,42] with the EachMovie dataset or [43,44,6,29] with MovieLens.”
Content-Based Collaborative Filtering for News Topic Recommendation (2015, methodology): “Hybrid of CF and Content-based Filtering As a first attempt to unify Collaborative Filtering and Content-based Filtering, (Basilico and Hofmann 2004) proposed to learn a kernel or similarity function between the user-item pairs that allows simultaneous generalization across either user or item dimen”
Personalized entity recommendation: a heterogeneous information network approach (2014, positive): “[20, 6, 1] incorporate item or user profiles (e.”
Multiple Aspect Ranking Using the Good Grief Algorithm (2007, methodology): “An extension of this model is provided by Basilico and Hofmann (2004) in the context of collaborative filtering.”
Support vector machines for collaborative filtering (2006, background): “There are two ways to cast the problem [1].”