Item-based collaborative filtering recommendation algorithms

By Badrul Munir Sarwar, George Karypis, J. Konstan, John Riedl. 2001

10,154 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research, Recommender Systems

How researchers see this paper

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

In Computer Networks and Communications

In Information Systems

In Management Science and Operations Research

In Recommender Systems

Citations in the corpus

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks (2017, methodology): “Typically, those methods look at the organic user behavior and build a predictor of the next event based on the last K user actions [17].”
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, methodology): “One of the main approaches that is employed in session-based recommendation and a natural solution to the problem of a missing user pro le is the item-to-item recommendation approach [12, 16].”
Neural Collaborative Filtering (2017, methodology): “While early literature on recommendation has largely focused on explicit feedback [30, 31], recent attention is increasingly shifting towards implicit data [1, 14, 23].”
Improved Recurrent Neural Networks for Session-based Recommendations (2016, methodology): “Neighbourhood based methods [22, 14] utilize the similarities between item and user purchase history; they can be applied to session-based recommendations by comparing session similarity.”
On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016, methodology): “Typically, one chooses the similarity matrix S∗ as a predefined function of R, such as cosine similarity (Sarwar et al. 2001; Linden, Smith, and York 2003): for item-based methods, this is given by”
Session-based Recommendations with Recurrent Neural Networks (2015, positive): “…employed in session-based 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…”
Poisoning Attacks to Graph-Based Recommender Systems (2018, background): “Neighborhood-based, association-rule-based, andmatrix-factorization-based recommender systems: Neighborhood-based recommender systems [27] nd neighbors of a user or neighbors of an item in order to recommend items to a user.”
Explainable Recommendation: A Survey and New Perspectives (2018, background): “In item-based collaborative filtering (Sarwar et al. [2001]), explanations can be provided by letting the users know that an item is recommended because it is similar to some other items that the user liked before, as shown in the left subfigure of Figure 2.3, where several similar movies that the u”
Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, methodology): “From item-based collaborative filtering [33] to matrix factorization techniques [21], various”
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, methodology): “CF relies on factorizing user-item interaction matrices to generate latent factors representing users and items [16, 17, 25, 30].”
Towards Making Systems Forget with Machine Unlearning (2015, methodology): “In particular, enabled by our approach, we created the first efficient unlearning algorithm for normalized cosine similarity [37, 63] commonly used by recommendation systems (e.g., LensKit) and for one-class support vector machine (SVM) [71] commonly used by classification/anomaly detection systems…”
Efficient Private Statistics with Succinct Sketches (2015, methodology): “Also note that users not involved in the protocol (or having limited “history”) can get recommendations too as the tally can still provide them with the global co-view matrix, which, even though it does not include their own contribution, can be used by the ItemKNN algorithm to derive recommendation”
SoRec: social recommendation using probabilistic matrix factorization (2008, background): “The most analyzed examples of memory-based collaborative filtering include userbased approaches [2, 7, 10, 25] and item-based approaches [4, 12, 19].”
Recommendation Systems in Software Engineering (2014, background): “[66] manually inspected explicit links between bug reports and change logs and extracted characteristic features of bug reports and code changes linking each other.”
On bootstrapping recommender systems (2010, background): “The item-item approach [7, 10] evaluates the preference of a user to an item based on ratings of “neighboring” items by the same user.”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Other collaborative filtering methods include a Bayesian model [20], a probabilistic relational model [37], a linear regression [91], and a maximum entropy model [75].”
A Survey of Collaborative Filtering Techniques (2009, methodology): “The neighborhood-based CF algorithm, a prevalent memory-based CF algorithm, uses the following steps: calculate the similarity or weight, wi, j , which reflects distance, correlation, or weight, between two users or two items, i and j; produce a prediction for the active user by taking the weighted ”
Recommender systems survey (2013, methodology): “The item to item version of the kNN algorithm significantly reduces the scalability problem [200].”