Amazon.com Recommendations: Item-to-Item Collaborative Filtering

By Greg Linden, Brent Smith, J. York. 2003

1,655 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Recommender Systems

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Image-Based Recommendations on Styles and Substitutes (2015, background): “There are a range of services such as Jinni2 which promise content-based recommendations for TV shows and similar media, but the features they expoit are based on reviews and metadata (such as cast, director etc.), and their ontology is handcrafted.”
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “Recommendation on (different) Amazon data has been discussed in [15] and [25].”
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, neutral): “One of the main approaches that is employed in session-based recommendation and a natural solution to the problem of a missing user profile is the item-to-item recommendation approach [13, 17].”
Session-based Recommendations with Recurrent Neural Networks (2015, methodology): “…recommendation and a natural solution to the problem of a missing user profile is the item-to-item recommendation approach (Sarwar et al., 2001; Linden et al., 2003) in this setting an item to item similarity matrix is precomputed from the available session data, that is items that are often…”
Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Lu et al. [2018] presented a deep learning recommendation model which co-learns user and item information from ratings and customer reviews by optimizing matrix factorization and an attention-based GRU network.”
In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, background): “These AI-infused decision-making processes include high-stakes domains such as prison sentence recommendation [14], credit decisions [32], and hiring [8], as well as more familiar lower-stakes domains such as personalized shopping [25] and music recommendation [23].”
SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “The most analyzed examples of memory-based collaborative filtering include userbased approaches [2, 7, 10, 25] and item-based approaches [4, 12, 19].”
Preventing shilling attacks in online recommender systems (2005, methodology): “In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks.”
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (2019, background): “Early approaches to CF consider the user–item rating matrix and predict ratings via user-based [11, 21] or item-based [15, 22] collaborative filtering methods.”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, methodology): “Therefore, the computational complexity of IPCC for an active user is O(n2).”
Neural Attentive Session-based Recommendation (2017, methodology): “Regularization is also included to avoid coincidental high similarities between rarely visited items [4, 20].”
Learning Tree-based Deep Model for Recommender Systems (2018, methodology): “Personalized recommendation method, based on the intuition that users’ interests can be inferred from their historical behaviors or other users with similar preference, has been proven to be effective in YouTube [7] and Amazon [22].”
Deep Reinforcement Learning for List-wise Recommendations (2017, background): “Collaborative ltering[11] is the most successful and the most widely used technique, which is based on the hypothesis that people oen get the best recommendations from someone with similar tastes to themselves[3].”
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (2009, methodology): “For industry applications, implementations at firms such as Amazon.com and CDNOW.com are described by Schafer et al. (1999) and Linden et al. (2003).”
GraphJet: Real-Time Content Recommendations at Twitter (2016, background): “, [29, 10, 1]) focus mostly on algorithms, as opposed to systems infrastructure—which is the perspective we take.”
TriRank: Review-aware Explainable Recommendation by Modeling Aspects (2015, methodology): “This is standard item-based CF, and has been used commercially by Amazon [16] and MovieLens [28].”