A Scalable Collaborative Filtering Framework Based on Co-Clustering

George, Thomas; Merugu, Srujana

doi:10.1109/icdm.2005.14

Cited by 306 publications

(206 citation statements)

References 4 publications

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“…George and Merugu [5] used a fast iterative heuristic proposed by Banerjee et al [1]. This algorithm has two phases: updating user clusters and updating item clusters.…”

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

“…In the online phase, the prediction is as follows: [5], incremental training is achieved by using new ratings to update the average parameters (r kl ,ru,r k ,ri,r l ) in equation (3). However, new users or items are not assigned to clusters during the online phase.…”

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

“…In collaborative filtering via co-clustering as suggested in [5], each user u is assigned to a user cluster (represented by ρ(u)) and each item i is assigned to an item cluster (represented by γ(i)) and the prediction is as follows:…”

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

“…We use an evolutionary coclustering algorithm that finds better solutions than Bregman co-clustering at the cost of offline training time, which is relatively unimportant. Also, we revise the incremental algorithm suggested in [5] and introduce an ensemble strategy to give better predictions.…”

Section: Introductionmentioning

confidence: 99%

“…George and Merugu [5] used co-clustering as a scalable incremental CF approach for dynamic settings. They showed the performance of incremental co-clustering is comparable to incremental SVD but much more scalable.…”

Section: Introductionmentioning

confidence: 99%

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Incremental collaborative filtering via evolutionary co-clustering

Khoshneshin

Street

2010

Proceedings of the Fourth ACM Conference on Recommender Systems

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Collaborative filtering is a popular approach for building recommender systems. Current collaborative filtering algorithms are accurate but also computationally expensive, and so are best in static off-line settings. It is desirable to include the new data in a collaborative filtering model in an online manner, requiring a model that can be incrementally updated efficiently. Incremental collaborative filtering via co-clustering has been shown to be a very scalable approach for this purpose. However, locally optimized co-clustering solutions via current fast iterative algorithms give poor accuracy. We propose an evolutionary co-clustering method that improves predictive performance while maintaining the scalability of co-clustering in the online phase.

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“…George and Merugu [5] used a fast iterative heuristic proposed by Banerjee et al [1]. This algorithm has two phases: updating user clusters and updating item clusters.…”

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

Section: Incremental Cf Via Co-clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incremental collaborative filtering via evolutionary co-clustering

Khoshneshin

Street

2010

Proceedings of the Fourth ACM Conference on Recommender Systems

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Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

Fayala¹,

Mezni

2019

Concurrency and Computation

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Summary With the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

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FSCOAL-Parallel simultaneous fuzzy co-clustering and learning

Biton

Kalech

Rokach

2018

Int. J. Intell. Syst.

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A model‐based co‐clustering divides the data based on two main axes and simultaneously trains a supervised model for each co‐cluster using all other input features. For example, in the rating prediction task of recommender system, the main two axes are items and users. In each co‐cluster, we train a regression model for predicting the rating based on other features such as user's characteristics (e.g., gender), item's characteristics (e.g., genre), contextual features (e.g., location), and so on. In reality, users and items do not necessarily belong to a single co‐cluster, but rather can be associated with several co‐clusters. We extend the model‐based co‐clustering to support fuzzy co‐clustering. In this setting, each item–user pair is associated to every co‐cluster with some membership grade. This grade indicates the level of relevance of the item–user pair to the co‐cluster. Furthermore, we propose a distributed algorithm, based on a map‐reduce approach, to handle big datasets. Evaluating the fuzzy co‐clustering algorithm on three datasets shows a significant improvement comparing with a regular co‐clustering algorithm. In addition, a map‐reduce version of the fuzzy co‐clustering algorithm significantly reduces the runtime.

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A Scalable Collaborative Filtering Framework Based on Co-Clustering

Cited by 306 publications

References 4 publications

Incremental collaborative filtering via evolutionary co-clustering

Incremental collaborative filtering via evolutionary co-clustering

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

FSCOAL-Parallel simultaneous fuzzy co-clustering and learning

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