Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations

Ahmed, Adeel; Saleem, Khalid; Khalid, Osman; Gao, Jiechao; Rashid, Umer

doi:10.1016/j.neucom.2022.09.023

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…Wang et al (2020b) created a cross-domain hierarchical recurrent model that integrates sequential information based on user and system interactions over a period to provide session-based recommendations. Ahmed et al (2022) proposed the trust-aware spatial-temporal activity-based denoising autoencoder, which provides item purchase recommendations with consideration for the precise time. In addition, they consider user location, sentiment analysis and level of trust.…”

Section: Related Workmentioning

confidence: 99%

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

Wang,

Justitia,

Wang

2023

DTA

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PurposeThe explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.Design/methodology/approachWe propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.FindingsExpanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.Research limitation/implicationsThis study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.Originality/valueThis study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

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Section: Related Workmentioning

confidence: 99%

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

Wang,

Justitia,

Wang

2023

DTA

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“…Makes recommendations across multiple domains, such as recommending products in one category based on user behavior in another category [81].…”

Section: Cross-domainmentioning

confidence: 99%

“…Trust-aware recommendations have also gained popularity, with researchers considering factors such as time, location, trust level, and sentiment analysis to generate recommendations [81]. For instance, Ahmed et al proposed an autoencoder-based deep learning model that considers these factors.…”

Section: I: Incorporating Knowledge and Trust For Accuracymentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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An Accurate Knowledge Service Recommendation Method for College Ideological Education Based on Data Portrait Technology

Jiang

2024

Learning and Analytics in Intelligent Systems

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Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations

Cited by 8 publications

References 41 publications

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

Deep Learning-Based Recommendation System: Systematic Review and Classification

An Accurate Knowledge Service Recommendation Method for College Ideological Education Based on Data Portrait Technology

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