User-Review Oriented Social Recommender System for Event Planning

Singh, Robert P.; Singh, Pardeep; Bathla, Gourav

doi:10.18280/isi.250514

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Content information such as time, status, situation, and mood of the user is the most important factor in proper recommendations. Location is also a very important and vital factor for the user; it has a great impact on suggestions based on the preferences of the user [64][65][66].…”

Section: Social Recommender Systemsmentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.

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Section: Social Recommender Systemsmentioning

confidence: 99%

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

2022

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“…Automatically mining of valuable information from text documents obtain enormous attention in many active research areas such as natural language processing (NLP), information retrieval and so forth 1 across the globe over the past decades. In NLP, sentiment analysis is one of the active research area that deals with opinions in text documents 2,3,4 . The purpose of sentiment analysis (referred as opinion mining also) 5 is to recognize the emotional label (i.e., negative, or positive, or neutral) of the text documents automatically 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Singh

Sachan

Patel

2022

Concurrency and Computation

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Cross-domain sentiment classification is a significant task of sentiment analysis that objectives to predict the opinion orientation of text documents in the target domain by using the source domain's learned classifier. Most of the existing approaches of domain-adaptation in sentiment classification focus on sharing low-dimensional features across the domain using domain independent and specific features to mitigate the gap between domains. Earlier cross-domain sentiment classification approaches mainly focused on document level and sentence level, they cannot consider the full impact of aspect words, position of the words, and long-term dependencies. To address this concern, we propose a model for cross-domain sentiment classification, which is based on decoding-enhanced BERT with disentangled attention (DeBERTa). DeBERTa is a pretrained language model based on transformer architecture. In this article, we perform sentence and aspect embedding to mine wordpiece information from text document. DeBERTa language-model utilize disentangled attention mechanism and an enhanced mask decoder to understand the expression features. Disentangled attention mechanism is used to encode each word into two vectors (i.e., content and position vector). In order to predict the masked tokens during model pretraining, an enhanced mask decoder is employed, which incorporates absolute positions in the decoding layer.Finally, experiments are conducted on the benchmark dataset that demonstrates the superiority of fine-tuned DeBERTa model for cross-domain sentiment classification tasks.

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“…However, the recent studies show that the main way to discover hot topics on micro-blog is to use clustering algorithm to cluster text content [19,20]. The traditional clustering algorithms mainly focus on large and traditional text, while the texts of microblog are very short, trendy, and network language.…”

Section: Introductionmentioning

confidence: 99%

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

Li-na

Wei

2021

Teh. vjesn.

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It was difficult to find the microblog hotspot because the characteristics of microblog were short, rapid, change and so on. A microblog hotspot detection method based on MFIHC and TOPSIS was proposed in order to solve the problem. Firstly, the calculation of HowNet similarity was used in the score function of FIHC, the semantic links between frequent words were considered, and the initial clusters based on frequent words were produced more accurately. Then the initial cluster of the text repletion of mircoblog was reduced, and the idea of Single-Pass clustering was used to the reduced topic cluster in order to get the Hotspot. At last, an improved TOPSIS model was used to sort the hot topics in order to get the rank of the hot topics. Compared with the other text clustering algorithms and hotspot detection methods, the method has good effect, and can be a more comprehensive response to the current hot topics.

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User-Review Oriented Social Recommender System for Event Planning

Cited by 12 publications

References 28 publications

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

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