Incremental collaborative filtering via evolutionary co-clustering

Khoshneshin, Mohammad; Street, W. Nick

doi:10.1145/1864708.1864778

Cited by 36 publications

(20 citation statements)

References 7 publications

(20 reference statements)

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“…Lastly Castro-Herrera et al (2009) implemented and evaluated a forum RS designed to handle the challenge of dynamically evolving internet forums which are characterized by a constant influx of new user and new posts. According to Khoshneshin and Street (2010) current RS are computationally expensive and therefore they work best in static offline settings. They propose an evolutionary co-clustering method that includes the new data in CF model in an online real time manner which improve predictive performance.…”

Section: Real-time Dynamicsmentioning

confidence: 99%

A study of the dynamic features of recommender systems

Rana¹,

Jain

2012

Artif Intell Rev

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The extensive usage of internet is fundamentally changing the way we live and communicate. Consequently, the requirements of users while browsing internet are changing drastically. Recommender Systems (RSs) provide a technology that helps users in finding relevant contents on internet. Revolutionary innovations in the field of internet and their consequent effects on users have activated the research in the area of recommender systems. This paper presents issues related to the changing needs of user requirements as well as changes in the systems' contents. The RSs involving said issues are termed as Dynamic Recommender Systems (DRSs). The paper first defines the concept of DRS and explores the various parameters that contribute in developing a DRS. The paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

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Section: Real-time Dynamicsmentioning

confidence: 99%

A study of the dynamic features of recommender systems

Rana¹,

Jain

2012

Artif Intell Rev

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“…The assumption here is that the number of co-clusters is always fixed, while our approach can possibly determine a different number of clusters. This work has been recently improved by [23] in which the authors suggest to estimate the clusters of the new incoming objects as soon as they arrive. They also propose an ensemble method for combining multiple local co-clustering results (with different number of clusters).…”

Section: Related Workmentioning

confidence: 99%

Hierarchical co-clustering: off-line and incremental approaches

Pensa

Ienco

Meo

2012

Data Min Knowl Disc

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Clustering data is challenging especially for two reasons. The dimensionality of the data is often very high which makes the cluster interpretation hard. Moreover, with high-dimensional data the classic metrics fail in identifying the real similarities between objects. The second challenge is the evolving nature of the observed phenomena which makes the datasets accumulating over time. In this paper we show how we propose to solve these problems.To tackle the high-dimensionality problem, we propose to apply a coclustering approach on the dataset that stores the occurrence of features in the observed objects. Co-clustering computes a partition of objects and a partition of features simultaneously. The novelty of our co-clustering solution is that it arranges the clusters in a hierarchical fashion, and it consists of two hierarchies: one on the objects and one on the features. The two hierarchies are coupled because the clusters at a certain level in one hierarchy are coupled with the clusters at the same level of the other hierarchy and form the co-clusters. Each cluster of one of the two hierarchies thus provides insights on the clusters of the other hierarchy.Another novelty of the proposed solution is that the number of clusters is possibly unlimited. Nevertheless, the produced hierarchies are still compact and therefore more readable because our method allows multiple splits of a cluster at the lower level. As regards the second challenge, the accumulating nature of the data makes the datasets intractably huge over time. In this case, an incremental solution relieves the issue because it partitions the problem. In this paper we introduce an incremental version of our algorithm of hierarchical co-clustering. It starts from an intermediate solution computed on the previous version of the data and it updates the co-clustering results considering only the added block of data. This solution has the merit of speeding up the computation with respect to the original approach that would recompute the result on the overall dataset. In addition, the incremental algorithm guarantees approximately the same answer than the original version, but it saves much computational load. We validate the incremental approach on several high-dimensional datasets and perform an accurate comparison with both the original version of our algorithm and with the state of the art competitors as well. The obtained results open the way to a novel usage of the co-clustering algorithms in which it is advantageous to partition the data into several blocks and process them incrementally thus "incorporating" data gradually into an on-going co-clustering solution.

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“…Co-clustering partitions two different kinds of objects simultaneously and is a data mining approach with applications in many areas such as recommender systems [8,6] and microarray analysis [11,5,3,4]. If one views the clustering problem as grouping rows of a matrix together, then co-clustering is the simultaneous grouping of rows and columns.…”

Section: Introductionmentioning

confidence: 99%

“…Examples include memetic algorithms [11], genetic algorithms [5], and evolutionary algorithms [3,4,8]. These works focus primarily on co-clustering gene expression data.…”

Section: Introductionmentioning

confidence: 99%

“…This is a more general case of the problem described in the literature, to which none of the above approaches are applicable. In the most relevant work [8], an evolutionary algorithm was used to solve the general co-clustering problem. However, this work focuses on optimizing a group of co-clustering solutions while our algorithm searches for individual solutions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Memetic Heuristic for the Co-clustering Problem

Khoshneshin

Ghazizadeh

Street

et al. 2015

Proceedings of the 8th International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONETICS

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Abstract. Co-clustering partitions two different kinds of objects simultaneously. Bregman co-clustering is a well-studied fast iterative algorithm to perform co-clustering. However, this method is very prone to local optima. We propose a memetic algorithm to solve the co-clustering problem. Experimental results show that this method outperforms the multi-start Bregman co-clustering in both accuracy and time.

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Incremental collaborative filtering via evolutionary co-clustering

Cited by 36 publications

References 7 publications

A study of the dynamic features of recommender systems

A study of the dynamic features of recommender systems

Hierarchical co-clustering: off-line and incremental approaches

A Memetic Heuristic for the Co-clustering Problem

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