RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis

Karn, Arodh Lal; Karna, Rakshha Kumari; Kondamudi, Bhavana Raj; Bagale, Girish; Pustokhin, Denis A.; Pustokhina, Irina V.; Sengan, Sudhakar

doi:10.1007/s10660-022-09630-z

Cited by 68 publications

(28 citation statements)

References 44 publications

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“…In addition, it provides customers with personalised experiences. For example, it uses recommendation systems to track customer feedback on products to better understand customer needs and preferences (Karn et al, 2022). Although, several studies addressed different aspects of the integration of IoT with e-commerce, no study has tried to understand the acceptance of IoT by users in the context of e-commerce.…”

Section: Iot and E-commerce Studiesmentioning

confidence: 99%

Integrating the Internet of Things Into E-Commerce

Picoto

Abreu

Martins

2023

International Journal of E-Business Research

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The emergence of the internet of things (IoT) is changing the behavior of people, companies, and their businesses. The IoT can enhance e-commerce by reducing the gaps between the virtual and physical worlds. Although the integration of the IoT into e-commerce has several positive effects, security concerns are greater in the IoT context than in traditional e-commerce settings. The proposed research model is based on the unified theory of acceptance and use of technology 2 (UTAUT2) along with trust, privacy, and confidential data concerns to explain the intention to use the IoT in e-commerce. The authors developed a questionnaire and received a total of 328 responses. The results show that among the IoT security factors, trust and privacy are significant determinants of the intention to use the IoT in e-commerce.

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Section: Iot and E-commerce Studiesmentioning

confidence: 99%

Integrating the Internet of Things Into E-Commerce

Picoto

Abreu

Martins

2023

International Journal of E-Business Research

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“…With the development of e-commerce, people are becoming increasingly accustomed to booking hotels online when they travel [1,2] . In the whole process of booking a hotel online, the reviews of other historical users of the target hotel provide great reference value for potential users [3,4] . Most of the existing online hotel booking platforms use star reviews as the main source of user feedback information.…”

Section: Introductionmentioning

confidence: 99%

Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

YANG,

MA,

WEI

et al. 2024

系统科学与信息学报(英文)

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To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm, this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information, and proposes a recommendation algorithm based on cloud model in probabilistic language environment. Initially, this paper quantifies the attributes in the review text based on the probabilistic linguistic term set. Subsequently, the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended, and the comprehensive evaluation number and attribute weight are

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“…The data created by these interactions holds latent analytical value. Extracting and dissecting this unstructured data have immense potential, particularly in the domain of sentiment analysis (SA)-a critical tool in various fields, from financial forecasting to public opinion monitoring and crisis management [3][4][5]. The limitations of unimodal data in conveying nuanced emotions necessitate the exploration of multimodal data for clearer affective communication, which carries profound social utility and significance [6,7].…”

Section: Introductionmentioning

confidence: 99%

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

Fan,

2024

IET Software

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In the rapidly evolving landscape of social media, the demand for precise sentiment analysis (SA) on multimodal data has become increasingly pivotal. This paper introduces a sophisticated data lake architecture tailored for efficient multimodal emotion feature extraction, addressing the challenges posed by diverse data types. The proposed framework encompasses a robust storage solution and an innovative SA model, multilevel spatial attention fusion (MLSAF), adept at handling text and visual data concurrently. The data lake architecture comprises five layers, facilitating real-time and offline data collection, storage, processing, standardized interface services, and data mining analysis. The MLSAF model, integrated into the data lake architecture, utilizes a novel approach to SA. It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. The integration of MLSAF with a scalable data lake architecture presents a strategic innovation poised to navigate the evolving complexities of social media data analytics.

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RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis

Cited by 68 publications

References 44 publications

Integrating the Internet of Things Into E-Commerce

Integrating the Internet of Things Into E-Commerce

Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

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