Incorporating Community Detection and Clustering Techniques into Collaborative Filtering Model

Deng, Wei; Patil, Rajvardhan; Najjar, Lotfollah; Shi, Yong; Chen, Zhengxin

doi:10.1016/j.procs.2014.05.246

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Similar to the success of integrating community information for link prediction, collaborative filtering tasks have previously shown success from the integration of network community structure [30].…”

Section: B Collaborative Filtering Experimentsmentioning

confidence: 98%

Stochastic block models with multiple continuous attributes

et al. 2019

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The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks.

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Section: B Collaborative Filtering Experimentsmentioning

confidence: 98%

Stochastic block models with multiple continuous attributes

et al. 2019

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“…Also, some works have incorporated the knowledge obtained from community detection into recommendation models, such as in Deng et al [26], using SVD-based techniques with information obtained from social communities; or Feng et al [29] where the recommendation is performed after time-weighted overlapping communities. Other approaches consider users' social relationships when suggesting personalised recommendations [82].…”

Section: Social Recommendationsmentioning

confidence: 99%

Beyond social graphs: mining patterns underlying social interactions

Baldominos

Calle

Cuadra

2016

Pattern Anal Applic

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This work aims at discovering and extracting relevant patterns underlying social interactions. To do so, some knowledge extracted from Facebook, a social networking site, is formalised by means of an Extended Social Graph, a data structure which goes beyond the original concept of a social graph by also incorporating information on interests. When the Extended Social Graph is built, state-of-the-art techniques are applied over it in order to discover communities. Once these social communities are found, statistical techniques will look for relevant patterns common to each of those, in such a way that each cluster of users is characterised by a set of common features. The resulting knowledge will be used to develop and evaluate a social recommender system, which aims at suggesting users in a social network with possible friends or interests.

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“…The increase in the items as well as users number leads in requiring more resources or slowing down the resources. One way to handle both the sparsity and the scalability issue in CF is by employing the SVD approach [8]. The SVD model helps in extracting latent features.…”

Section: Introductionmentioning

confidence: 99%

Community-Driven Collaborative Recommendation System

Chaudhary,

Singh

2019

IJRTE

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Recommendation systems (RSs) are an application of community detection, becoming more significant in our daily lives. They play a significant role in suggesting information to users such as products, services, friends and so on. A novel community driven collaborative recommendation system (CDCRS) has been proposed by the authors, in this particular paper. Furthermore, K means approach has been utilized to detect communities and extract the relationship among the users. The singular value decomposition method (SVD) is also applied. Issues of sparsity and scalability of the collaborative method are considered. Experiments were conducted on MovieLens datasets. Movie ratings were predicted and top-k recommendations for the user produced. The comparative study that was performed between the proposed as well as the collaborative filtering method dependent on SVD (CFSVD) as well as the results of experiments shows that CFSVD is outperformed by the proposed CDCRS method.

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Incorporating Community Detection and Clustering Techniques into Collaborative Filtering Model

Cited by 11 publications

References 8 publications

Stochastic block models with multiple continuous attributes

Stochastic block models with multiple continuous attributes

Beyond social graphs: mining patterns underlying social interactions

Community-Driven Collaborative Recommendation System

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