A context‐aware recommender method based on text and opinion mining

Sundermann, Camila Vaccari; Padua, Renan de; Tonon, Vitor Rodrigues; Marcacini, Ricardo Marcondes; Domingues, Marcos Aurélio; Rezende, Solange Oliveira

doi:10.1111/exsy.12618

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The authors evaluated their model using precision, average precision, recall, and F-score. A context-aware recommendation system based on text mining was developed in [94], where the researchers included positive, neutral, and negative reviews based on word embeddings. Also, they evaluated their model using mean average precision.…”

Section: Context-aware Recommendation Systems In E-tourismmentioning

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: Context-aware Recommendation Systems In E-tourismmentioning

confidence: 99%

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

Nawara

2024

Preprint

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“…The authors evaluated their model using precision, average precision, recall, and F-score. A context-aware recommendation system based on text mining was developed in [102], where the researchers included positive, neutral, and negative reviews based on word embeddings. Also, they evaluated their model using mean average precision.…”

Section: Context-aware Recommendation Systems In E-tourismmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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“…After this step, the next step is context extraction where the general attributes are extracted from reviewers. We used CIET.5embed techniques proposed in Reference 23. In this recommender system, the contextual information is extracted to define the values.…”

Section: Proposed Catc Systemmentioning

confidence: 99%

Context‐awaretext classification system to improve the quality of text: A detailed investigation and techniques

Saleem

Alhudhaif

Qureshi

et al. 2021

Concurrency and Computation

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Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context-aware text classification system to improve text quality. We use a content-aware recommendation system to extract the data from well-known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique onclassification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance.

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A context‐aware recommender method based on text and opinion mining

Cited by 6 publications

References 27 publications

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

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

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Context‐awaretext classification system to improve the quality of text: A detailed investigation and techniques

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