Search Results (196)

With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

Algorithmic transparency is widely considered essential for fostering trust in AI-based financial e-commerce services. However, empirical evidence remains limited on whether transparency benefits all consumers uniformly and how it is evaluated relative to other service attributes in realistic decision contexts. This study examines how consumers trade off transparency, personalization, and user control in robo-advisor (RA) services across different consumer segments. Through a discrete choice experiment and latent class logit modeling, two distinct segments are identified: selective high-expertise investors, who prioritize personalization and user control over transparency, and receptive general consumers, who respond strongly to enhanced explainability. These findings indicate that algorithmic transparency does not serve as a universal design solution but operates conditionally based on consumer expertise and attribute interactions. Simulation results further show that while a regulation-compliant, uniform service design may facilitate market entry, it constraints long-term expansion in heterogeneous markets. In contrast, a segment-based service portfolio calibrated to the distinct preferences of each group significantly increases overall adoption under the same regulatory constraints. These results suggest that sustainable AI diffusion in financial e-commerce requires a nuanced approach that balances disclosure with functional autonomy to address the diverse needs of both sophisticated and novice users. Full article

To address the one-class collaborative filtering (OCCF) issue in e-commerce recommendation with only positive implicit feedback, mainstream methods adopt pairwise preference learning represented by Bayesian Personalized Ranking (BPR). However, BPR relies on an invalid assumption and suffers from severe data sparsity. This paper proposes Multi-pairwise Ranking with Heterogeneous Implicit Feedback (MPIF), which exploits heterogeneous implicit and auxiliary information to mine deep user preferences, constructs six pairwise preferences for classified items, and optimizes the model via stochastic gradient descent (SGD). Experiments on three real-world datasets verify that MPIF+ outperforms all state-of-the-art baselines on Normalized Discounted Cumulative Gain at rank 5 (NDCG@5), Precision at rank 5 (Pre@5), Recall at rank 5 (Rec@5), and Area Under Curve (AUC). It yields maximum improvements of 34.2%, 5.5%, and 32.9% on NDCG@5 for the Sobazaar, Retailrocket, and REES46 datasets, respectively, achieving significant and stable recommendation gains. Full article

The rapid expansion of global e-commerce platforms has led to unprecedented volumes of heterogeneous, multimodal, and continuously evolving data, creating significant challenges for prediction, personalization, trust, and operational decision-making. Deep Learning has emerged as a core enabling technology for addressing these challenges, offering powerful representation learning, sequential reasoning, graph-based inference, and decision-centric optimization capabilities. This survey provides a comprehensive and decision-oriented review of recent advances in Deep Learning for e-commerce, covering consumer behavior prediction, demand forecasting, recommendation systems, sentiment and review intelligence, catalogue understanding, fraud detection, cybersecurity, and large-scale operational optimization. Beyond predictive and personalization tasks, the survey emphasizes decision intelligence, highlighting the growing role of Reinforcement Learning and integrated Artificial Intelligence systems in pricing, logistics, warehouse automation, and platform reliability. We organize the literature according to key e-commerce objectives and operational contexts, analyze methodological trends and deployment challenges, and discuss limitations related to scalability, robustness, interpretability, and cross-border adaptability. Finally, we identify open research directions toward unified multimodal foundation models, culturally adaptive intelligence, and trustworthy, sustainable Artificial Intelligence systems for next-generation e-commerce platforms. Full article

The rapid advancement of artificial intelligence (AI) in e-commerce has intensified data-driven personalization, raising important questions about its psychological implications for consumers and its role in shaping sustainable and responsible digital business practices. This study examines how AI-driven personalization affects consumer psychological well-being in the Turkish e-commerce market and investigates the roles of privacy concerns and brand trust in shaping this relationship from a social sustainability and responsible AI perspective. The research develops and empirically tests an integrated model comprising perceived personalization, privacy concerns, psychological well-being, and brand trust. Survey data from 400 active e-commerce customers were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings show that both perceived relevance and perceived specificity significantly enhance psychological well-being by reducing cognitive overload and increasing perceived value. However, these personalization dimensions also increase privacy concerns, with perceived specificity exerting a notably stronger effect. Privacy concerns negatively affect psychological well-being and competitively mediate the relationship between personalization and well-being, reflecting the Personalization–Privacy Paradox in AI-driven e-commerce contexts. Moreover, brand trust significantly moderates this dynamic by weakening the harmful impact of privacy concerns on psychological well-being. Overall, the findings indicate that privacy concerns represent a latent social cost that can undermine the long-term sustainability of data-intensive business models when not governed by trust-based mechanisms. Full article

With the increasing prominence of sustainable consumption and the rising influence of Generation Z in the fashion market, secondhand fashion e-commerce platforms have become essential carriers of green fashion. Although AI-assisted recommendation mechanisms are widely embedded in these platforms, their psychological and behavioral effects on users’ continuous use and social engagement remain insufficiently examined. To address this gap, this study incorporates the Stimulus–Organism–Response (SOR) framework to investigate the psychological reaction pathways and behavioral intentions of Generation Z users within Human-AI Collaboration-enabled green e-commerce environments. Three AI-driven service stimuli—Human-AI Collaborative Recommendation Perception, AI Interaction Transparency, and Perceived Personalization—were conceptualized as stimulus variables; Psychological Immersion, Emotional Triggering, Cognitive Engagement, and Platform Trust were modeled as organism variables; and Continuous Use Intention and Social Sharing Intention served as behavioral response variables. Based on 498 valid samples analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate strong empirical support for all proposed hypotheses. Specifically, AI-driven stimuli significantly and positively influence psychological responses, which subsequently strengthen users’ continuous usage and social sharing intentions. This research provides theoretical insights for developing Human-AI Collaboration-enabled service systems that balance efficiency and emotional resonance on green e-commerce platforms, and offers practical implications for promoting sustainable fashion values among younger consumers. Full article

In the world of e-commerce, ensuring customer satisfaction and retention depends on delivering an optimal user experience. As the primary point of contact between businesses and consumers, a user interface’s success hinges on personalized human–computer interaction. The goal of this paper is to introduce the concept of a self-adaptive multi-variant user interface based on a novel application of a three-way decision-making model, which allows for “accept”, “reject”, or “delay” decisions on UI changes. The proposed framework enables the delivery of a multi-variant e-commerce user interface. It leverages human-centered machine learning to identify homogeneous groups of customers for whom a layout tailored to their behavior can be offered. The functionality of the solution was verified through pilot implementation and experimental studies. The results positively validated the three-way decision algorithm and highlighted clear directions for its refinement. The primary contribution of this work is the novel adaptation of the three-way decision model to create an automated framework for e-commerce UI personalization, moving beyond the limitations of traditional binary A/B testing. This study demonstrates the practical feasibility of using a self-adaptive, multi-variant interface to significantly improve user experience and key business metrics. These results confirm the feasibility and effectiveness of using self-adaptive e-commerce interfaces to improve the user experience. The proposed framework represents a promising solution to the challenges posed by static interfaces and demonstrates the potential for wider application in the e-commerce domain and beyond. Full article

Currently, with the widespread popularization of e-commerce systems, enterprises have increasingly high requirements for the timeliness of order fulfillment. It has become particularly critical to enhance the operational efficiency of heterogeneous robotic “goods-to-person” (G2P) systems in book e-commerce fulfillment, reduce enterprise operational costs, and achieve highly efficient, low-carbon, and sustainable warehouse management. Therefore, this study focuses on determining the optimal storage location assignment strategy and order batching method. By comprehensively considering the characteristics of book e-commerce, such as small-batch, high-frequency orders and diverse SKU requirements, as well as existing system issues including uncoordinated storage assignment and order processing, and differences in the operational efficiency of heterogeneous robots, this study proposes a joint optimization framework for storage location assignment and order batching centered on a multi-objective model. The framework integrates the time costs of robot picking operations, SKU turnover rates, and inter-commodity correlations, introduces the STCSPBC storage strategy to optimize storage location assignment, and designs the SA-ANS algorithm to solve the storage assignment problem. Meanwhile, order batching optimization is based on dynamic inventory data, and the S-O Greedy algorithm is adopted to find solutions with lower picking costs. This achieves the joint optimization of storage location assignment and order batching, improves the system’s picking efficiency, reduces operational costs, and realizes green and sustainable management. Finally, validation via a spatiotemporal network model shows that the proposed joint optimization framework outperforms existing benchmark methods, achieving a 45.73% improvement in average order hit rate, a 48.79% reduction in total movement distance, a 46.59% decrease in operation time, and a 24.04% reduction in conflict frequency. Full article

Digital retail is undergoing a paradigm shift driven by the deep integration of artificial intelligence (AI) and augmented reality (AR). Although prior studies have examined the independent effects of AI-based personalized recommendation (cognitive path) and AR-enabled immersion (experiential path), how their integration systematically shapes user behavior through internal psychological mechanisms remains an important unresolved theoretical gap. To address this gap, this study develops an integrated model grounded in the stimulus–organism–response (S-O-R) framework and trust transfer theory. Specifically, the model examines how personalized recommendation, as a dynamic external stimulus, influences users’ cognitive state (perceived usefulness) and experiential state (immersion); how the overall trust of users in the integrated platform can be used as a key boundary condition to adjust the transformation efficiency from the above stimulus to the internal state; and how the above cognitive and experiential states can ultimately drive the continued usage intention through the mediation of positive emotional response. Based on survey data from 400 Chinese consumers with AR shopping experience on Taobao, analyzed using structural equation modeling (SEM), the results indicate that (1) personalized recommendation positively affects both immersion and perceived usefulness; (2) platform trust significantly and positively moderates the effects of personalized recommendation on both immersion and perceived usefulness; (3) both cognitive and experiential states stimulate positive emotions, which in turn enhance continued usage intention, with perceived usefulness exerting a stronger effect; (4) a key theoretical finding is that there is a significant positive correlation between perceived usefulness and immersion, revealing the coupling of psychological paths in an integrated environment; however, immersion does not moderate the effect of personalized recommendation on emotional responses, suggesting that the current integration mode emphasizes the formation of a stable psychological structure rather than real-time interaction. This study makes three contributions to the existing literature. First, it extends the application of S–O–R theory in a complex technological environment by analyzing the “organism” as a parallel and related cognitive-experience dual path and confirming its coupling relationship. Second, it elucidates the enabling role of trust as a moderating mechanism rather than a direct antecedent, thereby enriching micro-level evidence for trust transfer theory in the context of technology integration. Finally, by contrasting path coupling with process regulation, this study provides a more detailed distinction for understanding the theoretical connotations and boundaries of AI–AR technology integration, which may mainly be a kind of structural integration. Full article

Under the influence of the long-tail effect, market segmentation and personalized demand provide room for small brands to grow. Meanwhile, consumer behavior patterns have also shifted, with increased acceptance of low-priced, highly practical goods. This paper constructs a two-tier competitive supply chain model. The manufacturer invests in big data from e-commerce platforms and decides on the production of products by combining sales data and consumer preferences. The two retailers are a head brand retailer, which is larger, and a newcomer brand retailer, which is smaller, and both consider advertising to expand their markets. The paper distinguishes four types of advertising strategies (NA, R₁A, R₂A, BA). Secondly, the differential game model is used to discuss the optimal solutions of different advertising strategies under the relevant situations of demand perturbation and demand non-perturbation. Again, empirical analyses are used to verify the robustness of the model by fitting it with the simulation model. Finally, the paper further extends the model to the symmetric domain to explore the optimal retailer capacity in the market, and comes to the following conclusions (1) In the case of non-disturbed demand, the differences in retailer size and competitiveness can promote a more efficient allocation of resources, and the advertisements placed by small brands are the most effective in terms of market share and profitability, which can also improve the overall performance of the supply chain. (2) Demand perturbation makes the unilateral advertisers more susceptible to external disturbances, and the profit is uncertain while the advertisers’ investment increases. (3) In the expansion model, the maximum capacity of small-brand retailers is 3. When retailers exceed 3, it is difficult for other retail brands to enter the market. Full article

The expansion of digital technologies has led to a digital transformation of the economy and society. E-commerce, driven by new digital technologies and the restrictions during the COVID-19 pandemic, has increased its share in the overall trade of goods and services, influencing economic growth. This article examines the impact of emerging digital technologies such as artificial intelligence (AI), big data, the Internet of Things (IoT), and cloud computing (CC) on the e-commerce sector. Within this study, we explore the digital transformation of the EU economy, focusing on the impact of artificial intelligence (AI), big data, the Internet of Things (IoT), and cloud computing (CC) on e-commerce development and sustainable economic performance (GDP). The methodology employs a multilayer perceptron (MLP) neural network to model the non-linear, predictive relationship between digital adoption and e-commerce. Subsequently, hierarchical cluster analysis groups countries by digital maturity. The findings confirm that digital adoption is a significant and non-linear predictor of e-commerce, while the clustering reveals a pronounced regional heterogeneity in the capacity to translate technology into macro-economic performance. The research results show that by understanding and adopting these technologies, companies in the e-commerce field can gain a competitive advantage and better meet customer requirements and expectations. This adoption can lead to improved personalization of the shopping experience, increased operational efficiency, and enhanced customer satisfaction, ultimately resulting in better and sustainable economic performance. Full article

The rise of e-commerce platforms has completely revolutionized the way in which consumers interact with the market. In our digital world, due to the evolution of technology, people can purchase with ease the desired products, regardless of time and place, directly from their personal devices. This has led to a considerable improvement in users’ experiences, saving both time and money and avoiding stores’ congestions. At the same time, the emerging technologies, such as machine learning, artificial intelligence, augmented reality, and blockchain, registered a substantial contribution to optimizing e-commerce platforms by enhancing the efficiency of the processes, better understanding users’ needs, and offering personalized solutions. Therefore, the present bibliometric investigation aims to provide a comprehensive overview of the research domain-electronic commerce exploration using emerging technologies. Based on a dataset collected from the Web of Science database, the study reveals key details of the field, research characteristics, main themes, and current trends. Within the analysis, the R-tool—Biblioshiny 4.2.1—has been used for the creation of tables, graphs, and visual representations. The high importance of the domain, together with the significant interest within academics in publishing papers around this area, is validated by the value obtained for the annual growth rate, more specifically 44.65%, as well as by the cross-validation analyses performed in VOSviewer 1.6.20 and CiteSpace 6.3.R1, along with topic analysis performed through Latent Dirichlet Allocation and BERTopic. The results of this research represent precious information for the scientific community, authorities, and even companies that are oriented to e-commerce platforms, since crucial details about the market trends, domain’s impact, and key contributions are exposed. Full article

E-commerce platforms are rapidly transforming global consumer behavior, yet the psychological and demographic determinants of sustainable digital consumption remain underexplored. This study investigates how extraversion–introversion personality traits interact with demographic and socio-economic factors to influence e-commerce usage among Korean consumers, with implications for sustainable consumption practices. Based on data from the 13th Korea Media Panel Survey (2022), the results of this study indicate that extraverted individuals, women, younger consumers, higher-educated and higher-income groups, employed and unmarried individuals, those in larger households, and urban residents were more likely to engage in e-commerce, whereas introverts and older adults showed lower participation. These findings highlight the complex interplay between psychological dispositions and structural conditions in shaping digital consumption. This study advances theoretical understandings of the issue by integrating extraversion–introversion traits and demographic variables into a multidimensional framework of consumer behavior. Practically, it emphasizes the need for inclusive e-commerce design: socially interactive features for extraverts, information-rich streamlined interfaces for introverts, and enhanced accessibility for older or rural users. Policy implications include promoting digital literacy, reducing infrastructure inequalities, and implementing ethical, personality-informed personalization strategies to foster equitable and sustainable online commerce. This research contributes to sustainable consumer intelligence by demonstrating how psychological and contextual factors jointly influence e-commerce engagement. Full article