Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

Jayalakshmi, N.; Padmaja, P.; Suma, G. Jaya

doi:10.1142/s0218001420530031

Cited by 4 publications

(12 citation statements)

References 30 publications

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“…The existing methodologies like FCM clustering, K‐means clustering, mean shift clustering (MSC), hierarchical clustering (HC), and density peak clustering (DPC) are implemented in this article to estimate the performance of the proposed technique with All the news datasets. Estimation on MNSBC and Weblog databases are compared with K‐means, FCM‐recom prob, FCM‐KNN and LKNN 36 using precision, recall, and F1‐measure.…”

Section: Simulation Outcomes and Discussionmentioning

confidence: 99%

“…The proposed methodology achieves 93.03% precision for the fourth query. At the same time, conventional approaches like K-means, FCM-KNN, FCM-recom prob, and LKNN 36 Comparison is taken against K-means, FCM-KNN, FCM-recom prob, and LKNN. 36 Figure 9A shows precision analysis taken for four different queries.…”

Section: Analysis Of All News Datasetmentioning

confidence: 99%

“…At the same time, conventional approaches like K-means, FCM-KNN, FCM-recom prob, and LKNN 36 Comparison is taken against K-means, FCM-KNN, FCM-recom prob, and LKNN. 36 Figure 9A shows precision analysis taken for four different queries. The proposed methodology achieves 90.02% precision for fourth query, while conventional approaches like K-means, FCM-recom prob, FCM-KNN, and LKNN 36 accomplish 38.86%, 41.02%, 60.01%, and 88.72% precision for fourth query.…”

Section: Analysis Of All News Datasetmentioning

confidence: 99%

“…The drawback noticed was not capable of generating meaningful user recommendations. Interesting subgraphs and Laplace based k‐nearest neighbor (LKNN) algorithm was suggested by Reference 36 for the WPR system. Chronological cuckoo search (Chronological‐CS) algorithm and LKNN were suggested in this article to retrieve a useful web page from an interesting webpage.…”

Section: Related Workmentioning

confidence: 99%

“…Figure8A-C displays the performance comparison of precision, recall, and F1-measure against different user queries with 80% training data. Evaluation of the MNSBC database is compared with K-means, FCM-KNN, FCM-recom prob, and LKNN36 to show the efficiency of the proposed methodology.Figure8Ashows precision analysis taken for four different queries. The proposed methodology achieves 90.07% precision for fourth query, while conventional approaches like, K-means, FCM-KNN, FCM-recom prob, and LKNN 36 accomplish 37.07%, 40.12%, 58.48%, and 86.76% precision for fourth query.…”

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confidence: 99%

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Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Alagappan

Victor

2022

Concurrency and Computation

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Web-page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph-based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot-based dynamic graph model obtains the user-web page relationship. Weight calculation of user-item and the user-user edge is also processed with this graph model. The user-user edge similarity is used to obtain the user preference vector. Then, unsupervised-deep auto encoder based density-based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. "All the news", MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database. K E Y W O R D S clustering, density based clustering, dynamic graph-based model, ranking, unsupervised-deep auto encoder, user preference vector 1 INTRODUCTION Recently, recommendation systems (RSs) are largely utilized in several web related applications. The RS is one of the software platforms that renders different kinds of ideas to the user depending on their requirement. RS is also called an information filtering mechanism, which seems like what type of music to listen to, what type of book to read, what type of things to buy and so forth. 1 In the internet, the overloading of information is

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Section: Simulation Outcomes and Discussionmentioning

confidence: 99%

Section: Analysis Of All News Datasetmentioning

confidence: 99%

Section: Analysis Of All News Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Alagappan

Victor

2022

Concurrency and Computation

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A Recommendation Method For Fusion of Multiple HIN Meta-Paths

Jin

et al. 2022

2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA)

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Improve Session-Based Recommendation with Triplet Mining and Dynamic Perturbations Graph Neural Networks

Zhu

Feng

Zhou

et al. 2023

Int. J. Patt. Recogn. Artif. Intell.

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Session-based recommendation (SBR) emphasizes mining user interests to predict the next click based on recent interactions within sessions. Most current SBR methods suffer from insufficient interactive information problems and fail to distinguish session representations with high similarities, which can neglect the inherent features within sessions. To fill the gap, we propose a triplet mining enhanced graph neural networks (TME-GNN) approach to enhance the recommendation systems by mining structural and inherent information. Technically, we first generate anchor, positive and negative embeddings based on the given session and set a triplet mining task to improve the recommendation task with subtle features by pushing positive pairs close and pulling negative pairs away. Second, to robust the model, we employ a self-supervised auxiliary task by adding dynamic perturbations to the embedding space. We conduct extensive experiments to demonstrate the superiority of our method against other state-of-the-art algorithms. Our implementations are available on the following site https://github.com/Info4Rec/TME-GNN .

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Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

Cited by 4 publications

References 30 publications

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

A Recommendation Method For Fusion of Multiple HIN Meta-Paths

Improve Session-Based Recommendation with Triplet Mining and Dynamic Perturbations Graph Neural Networks

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