Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering

By Badrul Munir Sarwar, George Karypis, J. Konstan, John Riedl. 2002

518 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/43471090c5b8db10905addbdaede49ebc51b8515

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Citations in the corpus

A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering (2010, methodology): “Once the user clustering is complete, however, performance can be very good, since the size of the group that must be analyzed is much smaller [18].”
Data Mining Methods for Recommender Systems (2015, methodology): “[26] describe an approach to implement a scalable kNN classifier.”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, background): “This approach is an effective way to reduce sparsity and improve performance [44, 133, 144, 162].”
Short and tweet: experiments on recommending content from information streams (2010, background): “For example, Sarwar et al. applied clustering algorithms to partition user population, built neighborhoods for users from the partition, and considered only those neighborhoods when computing recommendations [18].”
A Survey of Collaborative Filtering Techniques (2009, methodology): “[43] and O’Connor and Herlocker [42] use clustering techniques to partition the data into clusters and use a memory-based CF algorithm such as a Pearson correlation-based algorithm to make predictions for CF tasks within each cluster.”
Diversity in recommender systems - A survey (2017, background): “…on only a few selected content items, but since the numbers of users and content items in most systems, such as Youtube for example [36,37] , range in millions if not billions this still means there is an immense data structure that usually cannot be processed without specialized algorithms [38] .”
ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines (2007, methodology): “The dataset comes with five predefined splitting, each uses 80% of the ratings for the training set and 20% for the test set (as described in [Sarwar et al., 2002]).”
Recommender Systems (2016, unknown): “As a result, it has become increasingly essential to design recommender systems that can perform effectively and efficiently in the presence of large amounts of data [527, 528, 587].”