Context-Aware Recommender Systems Based on Item-Grain Context Clustering

Shi, Yilong; Lin, Hong; Li, Yuqiang

doi:10.1007/978-3-319-63004-5_1

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…K-Means is based on pre-setting clusters' centroids, where based on the distance between each data point and the centroid, a cluster is formed. That step is repeated till convergence [133]. As we mentioned before, this algorithm is noted to be the simplest and most used clustering algorithm.…”

Section: K-means Partitional Clustering Algorithmmentioning

confidence: 99%

MCARS-CC: A Scalable Multi-context-aware Recommender System

Nawara

2024

Preprint

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<p>Recommendation Systems (RSs) have proved a compelling performance to overcome the data overload problem. Context-aware recommenders guide users/clients to more personalized recommendations. Incorporating contextual features in recommendation systems improves the systems' accuracy; however, they still suffer from sparsity and scalability problems which impact the quality of recommendations. To overcome these limitations, we propose a multi-context-aware recommendation system using the notion of consensus clustering, named MCARS-CC. Experimental results on real-world datasets show that the concept of consensus learning using clustering analysis can significantly improve the recommender systems' accuracy, handle sparsity in data, and address scalability for large-scale datasets. The proposed method surpasses the other recommendation algorithms in terms of accuracy, precision and recall. The MAE and RMSE results show that consensus clustering leads to better accuracy measures and a more stable resilient recommendation system; in particular, incorporating Hyber-Graph with Partitioning (HGPA) Consensus has improved MAE and RMSE by 25.96% and 8.94%, respectively, in Yelp-Dataset.</p>

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Section: K-means Partitional Clustering Algorithmmentioning

confidence: 99%

MCARS-CC: A Scalable Multi-context-aware Recommender System

Nawara

2024

Preprint

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“…Context-aware recommender systems (CARS) have emerged as a hot research topic in the field of recommendation, with the goal of enhancing recommendation quality and user loyalty by incorporating context knowledge. Integrating context information into recommendation structures is complex due to the high dimensionality of context information and the sparsity of results (Shi et al, 2017). The study looked at the evolution of travel advisory systems, as well as their usefulness and existing shortcomings.…”

Section: Introductionmentioning

confidence: 99%

The Impact of ICT Acquisition, Online courses, and Management System on Cultural Differences and English Language as Related to the Moderator Effort Expectancy

2021

QZJ

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In this paper, buyers are looking for reviews of the products before purchasing them. In view of this, online shopping platforms are encouraging their customers to provide reviews on products that would help future customers and the service provider to enhance their services. These reviews are normally in natural language mostly in the English language. These are used to analyze and provide data that is used for repairing and building new products because most services are unable to review consumers' reviews at the same time regularly, so they need mining tools to learn about those reviewers, which is what consumers need for their goods. Users review assessments for upgrading their products. Reviews are analyzed by customers to decide whether to purchase or not to purchase. The main objectives of this study are to develop a recommendation system based on customer reviews, develop a dataset of customer reviews from Amazon and eBay, analyze the reviews to create a database of products, develop an algorithm for generating positive/negative scores for a product, develop a method for gathering user requirements in natural language and to identify the main product, and also develop an algorithm to match the user request with the product and generate

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A Comprehensive Context-Aware Recommender System Framework

Inzunza

Juárez‐Ramírez

2018

Computer Science and Engineering—Theory and Applications

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Context-Aware Recommender Systems Based on Item-Grain Context Clustering

Cited by 4 publications

References 13 publications

MCARS-CC: A Scalable Multi-context-aware Recommender System

MCARS-CC: A Scalable Multi-context-aware Recommender System

The Impact of ICT Acquisition, Online courses, and Management System on Cultural Differences and English Language as Related to the Moderator Effort Expectancy

A Comprehensive Context-Aware Recommender System Framework

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