A clustering based approach to improving the efficiency of collaborative filtering recommendation

Liao, Chih-Lun; Lee, Shie-Jue

doi:10.1016/j.elerap.2016.05.001

Cited by 96 publications

(43 citation statements)

References 26 publications

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“…For example, in electronic text processing [11][12][13], clustering is used to reduce the dimensionality in order to improve the efficiency of the processing, or to ease the curse of dimensionality encountered for high-dimensional problems. In the recommendation applications in e-commerce [14], the size of the information matrix is reduced by clustering to enhance the efficiency of making recommendations. In power systems, clustering helps predict the trend of electricity demand in the future [15].…”

Section: Introductionmentioning

confidence: 99%

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Weighted z-Distance-Based Clustering and Its Application to Time-Series Data

Wang

Lin

et al. 2019

Applied Sciences

Self Cite

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Clustering is the practice of dividing given data into similar groups and is one of the most widely used methods for unsupervised learning. Lee and Ouyang proposed a self-constructing clustering (SCC) method in which the similarity threshold, instead of the number of clusters, is specified in advance by the user. For a given set of instances, SCC performs only one training cycle on those instances. Once an instance has been assigned to a cluster, the assignment will not be changed afterwards. The clusters produced may depend on the order in which the instances are considered, and assignment errors are more likely to occur. Also, all dimensions are equally weighted, which may not be suitable in certain applications, e.g., time-series clustering. In this paper, improvements are proposed. Two or more training cycles on the instances are performed. An instance can be re-assigned to another cluster in each cycle. In this way, the clusters produced are less likely to be affected by the feeding order of the instances. Also, each dimension of the input can be weighted differently in the clustering process. The values of the weights are adaptively learned from the data. A number of experiments with real-world benchmark datasets are conducted and the results are shown to demonstrate the effectiveness of the proposed ideas.Many types of clustering algorithms have been proposed [2,17]. Similarity or distance measures are core components to cluster similar data into the same clusters, while dissimilar or distant data are placed into different clusters [18]. Centroid-based clustering [19][20][21][22][23][24][25][26] groups data instances in an exclusive way. If an instance belongs to a definite cluster, it could not be included in another cluster. K-means is one such algorithm, well-known in the AI community. To use it, the user has to provide the desired number of clusters, K, in advance. Each instance is assigned to the nearest cluster center. Then the K cluster centers are re-estimated. This process is repeated until the cluster centers are stable. Self-organizing mapping (SOM) employs a set of representatives. When a vector is presented, all representatives compete with each other. The winner is updated so as to move toward the vector. Hierarchical clustering [27][28][29][30][31][32] creates a hierarchical decomposition of the set of data instances using some criteria. Two strategies, bottom-up and top-down, are adopted in hierarchical algorithms. The user usually has to decide how many and what clusters are most desirable from the offered hierarchy of clusters. Distribution-based clustering [33][34][35][36][37][38] is based on distribution models. Fuzzy C-means uses fuzzy sets to cluster instances, and data instances are bound to each cluster by means of a membership function. Therefore, each instance may belong to several clusters with different degrees of membership. Gaussian mixture model-Expectation maximization (GMM-EM) uses a completely probabilistic approach. Each cluster is mathematically represented by a parametr...

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Section: Introductionmentioning

confidence: 99%

“…Sci. 2019, 9, 5469 3 of 25 Lee and Ouyang proposed a self-constructing clustering (SCC) algorithm [20] which has been applied in various applications [4,6,13,14,16,55]. SCC is an exclusive clustering method.…”

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confidence: 99%

Weighted z-Distance-Based Clustering and Its Application to Time-Series Data

Wang

Lin

et al. 2019

Applied Sciences

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“…There are several research studies to improve the performance of CF in cold-start problem, such as combining multiple CF techniques [27] or performing CF on a small cluster rather than the entire graph [28].…”

Section: Related Workmentioning

confidence: 99%

dTrust: A Simple Deep Learning Approach for Social Recommendation

Dang

Ignat

2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. dTrust: a simple deep learning approach for social recommendationQuang-Vinh Dang, Claudia-Lavinia IgnatTo cite this version: Abstract-Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationships. However, these prediction approaches mostly rely on user personal information which is a privacy threat.In this paper, we present dTrust, a simple social recommendation approach that avoids using user personal information. It relies uniquely on the topology of an anonymized trust-useritem network that combines user trust relations with user rating scores. This topology is fed into a deep feed-forward neural network. Experiments on real-world data sets showed that dTrust outperforms state-of-the-art in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores for both warmstart and cold-start problems.

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“…Current personalization and recommendation techniques in cloud environments are based on rule-based systems, content-based and collaborative filtering [7][8][9][10]. However, solutions to mobile cloud service personalization expect services to adapt to the user's functional needs, the device in-use, and the user's context.…”

Section: Introductionmentioning

confidence: 99%

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

et al. 2018

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Cloud service providers typically compose their services from a number of elementary services, which are developed in-house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user's mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context criteria that combine cloudservice provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

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A clustering based approach to improving the efficiency of collaborative filtering recommendation

Cited by 96 publications

References 26 publications

Weighted z-Distance-Based Clustering and Its Application to Time-Series Data

Weighted z-Distance-Based Clustering and Its Application to Time-Series Data

dTrust: A Simple Deep Learning Approach for Social Recommendation

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

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