Knowledge based topic retrieval for recommendations and tourism promotions

Mishra, Ram Krishn; Jothi, J. Angel Arul; Urolagin, Siddhaling; Irani, Kayan

doi:10.1016/j.jjimei.2022.100145

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…Therefore, this study follows this traditional theoretical framework, focusing on whether and how cognitive (knowledge, information), affective (fear), efficacy and social norms are associated with COVID-19 vaccine acceptance. The theoretical model extends the previous Cognitive-Affective-Normative framework in the context of vaccine acceptance [ 1 ] by incorporating information inconsistency [ 8 , 9 , 26 , 27 ], subjective knowledge [ 6 , 7 , 26 , 28 , 29 ] and two facets of COO: product country image (PCI) and overall country image (OCI) [ 5 ]. The proposed model with hypotheses is presented in Fig.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Country of origin effects in explaining motivations for COVID-19 vaccine acceptance: A cognitive-affective-norm approach

Olsen,

Tuu

2024

Heliyon

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Section: Theoretical Frameworkmentioning

confidence: 99%

Country of origin effects in explaining motivations for COVID-19 vaccine acceptance: A cognitive-affective-norm approach

Olsen,

Tuu

2024

Heliyon

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“…Experimental results showed that LB-MMT outperformed all baseline models in a quantitative evaluation and had significant practical implications. Mishra et al [32] discussed the development of a digital tourism recommendation system, which utilized online comments, blogs, and rating data for analysis. A machine-learning-based system was established to achieve three subgoals: predicting star ratings from comments, a feedback model, and a knowledge-based recommendation system.…”

Section: Related Workmentioning

confidence: 99%

A Neural Topic Modeling Study Integrating SBERT and Data Augmentation

et al. 2023

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Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in short texts, the quality of the topics obtained by the models is low and incoherent, which is caused by the reduced word frequency (insufficient word co-occurrence) in short texts compared to long texts. To address these issues, we propose a neural topic model based on SBERT and data augmentation. First, our proposed easy data augmentation (EDA) method with keyword combination helps overcome the sparsity problem in short texts. Then, the attention mechanism is used to focus on keywords related to the topic and reduce the impact of noise words. Next, the SBERT model is trained on a large and diverse dataset, which can generate high-quality semantic information vectors for short texts. Finally, we perform feature fusion on the augmented data that have been weighted by an attention mechanism with the high-quality semantic information obtained. Then, the fused features are input into a neural topic model to obtain high-quality topics. The experimental results on an English public dataset show that our model generates high-quality topics, with the average scores improving by 2.5% for topic coherence and 1.2% for topic diversity compared to the baseline model.

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“…They collected restaurant data and used weight-based score calculation and cosine similarity matrix to build the recommendation system, which also suggests similar restaurants. Mishra et al [29] introduced an automated framework encompassing three distinct subtasks: star rating prediction based on reviews, a feedback model, and a knowledge-based recommender system. Their empirical investigation revealed that random forest (RF) and Decision Tree classifiers exhibited the most favourable accuracy levels for star rating prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Mishra et al. [29] introduced an automated framework encompassing three distinct subtasks: star rating prediction based on reviews, a feedback model, and a knowledge‐based recommender system. Their empirical investigation revealed that random forest (RF) and Decision Tree classifiers exhibited the most favourable accuracy levels for star rating prediction.…”

Section: Related Workmentioning

confidence: 99%

Recurrent ALBERT for recommendation: A hybrid architecture for accurate and lightweight restaurant recommendations

Keya,

Arpona,

Kabir

et al. 2023

Cognitive Comp and Systems

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The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision‐making method. With the help of collaborative filtering, some user‐based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences‐based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1‐score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily.

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Knowledge based topic retrieval for recommendations and tourism promotions

Cited by 19 publications

References 27 publications

Country of origin effects in explaining motivations for COVID-19 vaccine acceptance: A cognitive-affective-norm approach

Country of origin effects in explaining motivations for COVID-19 vaccine acceptance: A cognitive-affective-norm approach

A Neural Topic Modeling Study Integrating SBERT and Data Augmentation

Recurrent ALBERT for recommendation: A hybrid architecture for accurate and lightweight restaurant recommendations

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