An algorithmic framework for performing collaborative filtering

By Jonathan L. Herlocker, J. Konstan, Al Borchers, John Riedl. 1999

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Citations in the corpus

Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (2012, methodology): “The memory-based approaches use either user-based approaches (Herlocker et al., 1999) or item-based approaches (Karypis, 2001) for prediction (recommendation) of ratings for items.”
Freeze-Thaw Bayesian Optimization (2014, methodology): “Probabilistic Matrix Factorization As a ﬁnal experiment, we optimize three hyperparameters of a probabilistic matrix factorization (PMF) [24] on 100,000 ratings from the MovieLens data set [25].”
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].”
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, methodology): “An algorithmic framework for performing collaborative filtering is presented in [76].”
Explaining collaborative filtering recommendations (2000, background): “Yet explaining similarity is tricky, since the statistical similarity metrics that have been demonstrated as the most accurate such as correlation[5] are hard to understand for the average user.”
Analysis of recommendation algorithms for e-commerce (2000, background): “The size of the neighborhood has signi cant impact on the recommendation quality [14].”
Time-aware point-of-interest recommendation (2013, unknown): “, [8, 12,17]) have been proposed.”
Seventh IEEE International Conference on Data Mining Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights (background): “The original neighborhood-based approach, which was shared by virtually all earlier CF systems, is user-oriented; see [8] for a good analysis.”
Understanding and improving automated collaborative filtering systems (2000, methodology): “The field of Information Retrieval traditionally develops storage, indexing, and retrieval technology for textual documents.”
A new user similarity model to improve the accuracy of collaborative filtering (2014, methodology): “To solve this problem, weighted Pearson correlation coefﬁcient has been proposed [16].”
Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, unknown): “5 As in neighborhood-based CF [18]”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “This measure is similar to cosine similarity, except individual ratings are normalized by the vector average [13, 61].”
Privacy-preserving collaborative filtering using randomized perturbation techniques (2003, methodology): “2 Evaluation Criteria Several evaluation criteria for collaborative filtering have been used in literature [5, 10, 17].”
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences (2005, methodology): “Some of them frequently used are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Correlation between ratings and predictions [19].”
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization (2013, background): “The most analyzed examples of neighborhood-based collaborative filtering include user-based approaches [21], [37], item-based approaches [38], [39], and their fusion [40], [23].”
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization (2004, background): “Collaborative filtering technology (Herlocker et al., 1999; Konstan et al., 1997; Shardanand and Maes, 1995), generally involves matching, in real time, the ratings of a current user for objects (e.g., movies or products) with those of similar users (nearest neighbors) in order to produce…”
UserRec: A User Recommendation Framework in Social Tagging Systems (2010, unknown): “Two trends have rised in collaborative ﬁltering: one is memory-based algorithms (Herlocker et al. 1999), and the other is model-based algorithms (Hofmann 2004).”