Hasil untuk "Commerce"

Wei-Tsong Wang, Yi-Shun Wang, En-Ru Liu

Chaosheng Dong, Michinari Momma, Yijia Wang et al.

On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address this, we propose a simple yet effective behavior feature boosting method that leverages substitute relationships among products (BFS). BFS identifies substitutes-products that satisfy similar user needs-and aggregates their behavioral signals (e.g., clicks, add-to-carts, purchases, and ratings) to provide a warm start for new items. Incorporating these enriched signals into ranking models mitigates cold-start effects and improves relevance and competitiveness. Experiments on a large E-commerce platform, both offline and online, show that BFS significantly improves search relevance and product discovery for cold-start products. BFS is scalable and practical, improving user experience while increasing exposure for newly launched items in E-commerce search. The BFS-enhanced ranking model has been launched in production and has served customers since 2025.

Matteo Tolloso, Davide Bacciu, Shahab Mokarizadeh et al.

We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in forecasting purchase sequences and handling multi-interaction scenarios, achieving efficient personalized recommendations under real-world constraints.

Virginia Negri, Víctor Martínez Gómez, Sergio A. Balanya et al.

Product information extraction is crucial for e-commerce services, but obtaining high-quality labeled datasets remains challenging. We present a systematic approach for generating synthetic e-commerce product data using Large Language Models (LLMs), introducing a controlled modification framework with three strategies: attribute-preserving modification, controlled negative example generation, and systematic attribute removal. Using a state-of-the-art LLM with attribute-aware prompts, we enforce store constraints while maintaining product coherence. Human evaluation of 2000 synthetic products demonstrates high effectiveness, with 99.6% rated as natural, 96.5% containing valid attribute values, and over 90% showing consistent attribute usage. On the public MAVE dataset, our synthetic data achieves 60.5% accuracy, performing on par with real training data (60.8%) and significantly improving upon the 13.4% zero-shot baseline. Hybrid configurations combining synthetic and real data further improve performance, reaching 68.8% accuracy. Our framework provides a practical solution for augmenting e-commerce datasets, particularly valuable for low-resource scenarios.

Milon Bhattacharya, Milan Kumar

Indias e-commerce market is projected to grow rapidly, with last-mile delivery accounting for nearly half of operational expenses. Although vehicle routing problem (VRP) based solvers are widely used for delivery planning, their effectiveness in real-world scenarios is limited due to unstructured addresses, incomplete maps, and computational constraints in distance estimation. This study proposes a framework that employs large language models (LLMs) to critique VRP-generated routes against policy-based criteria, allowing logistics operators to evaluate and prioritise more efficient delivery plans. As a illustration of our approach we generate, annotate and evaluated 400 cases using large language models. Our study found that open-source LLMs identified routing issues with 79% accuracy, while proprietary reasoning models achieved reach upto 86%. The results demonstrate that LLM-based evaluation of VRP-generated routes can be an effective and scalable layer of evaluation which goes beyond beyond conventional distance and time based metrics. This has implications for improving cost efficiency, delivery reliability, and sustainability in last-mile logistics, especially for developing countries like India.

Haoxin Wang, Xianhan Peng, Xucheng Huang et al.

In this paper, we introduce ECom-Bench, the first benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. ECom-Bench features dynamic user simulation based on persona information collected from real e-commerce customer interactions and a realistic task dataset derived from authentic e-commerce dialogues. These tasks, covering a wide range of business scenarios, are designed to reflect real-world complexities, making ECom-Bench highly challenging. For instance, even advanced models like GPT-4o achieve only a 10-20% pass^3 metric in our benchmark, highlighting the substantial difficulties posed by complex e-commerce scenarios. The code and data have been made publicly available at https://github.com/XiaoduoAILab/ECom-Bench to facilitate further research and development in this domain.

Hyunwoo Hwangbo, Yang Sok Kim, K. Cha

Юлія Нежид

Статтю присвячено актуальним питанням, що пов’язані з диджиталізацією системи обліково-інформаційного забезпечення в умовах сьогодення. Доведено важливість цифрової трансформації у контексті сучасного бізнес-середовища включаючи вплив війни на цей процес. Розглянуто і проаналізовано найпопулярніші програмні рішення та онлайн-сервіси, які зараз використовуються для забезпечення системи обліково-інформаційного забезпечення. Визначено та схарактеризовано основні цифрові технології, які можна впроваджувати у сферу обліку як окремо, так і в кооперації. Продемонстровано ключові перешкоди, які впливають на процес диджиталізації обліку. Наведено перелік основних переваг диджиталізації обліку в умовах війни, що підкреслює її важливість у процесі забезпечення ефективного та безперервного управління діяльністю підприємства.

Kanika Kohli, Archana Tyagi, Poonam Khurana et al.

The model of excellencism and perfectionism (MEP) theorizes that the attitude toward goals as characterized in excellencism is desirable over perfectionism. Using the self-determination theory (SDT), this study aims to investigate the varying effects of perfectionism and excellencism on work engagement and performance. The study used a time-lagged multi-phase, multi-source, and cross-sectional online survey to collect responses from 360 corporate employees of Indian companies in the services industry. The results indicate that while both perfectionism and excellencism entail pursuing high standards, they relate differently with performance and work engagement. Interestingly, excellencism and work engagement were significantly associated with performance (p < .001); however, perfectionism was insignificant (p = .989). Perfectionism strengthens work engagement (β = 0.112; p = .013), while excellencism has an insignificant effect (β = 0.035; p = .537). Work engagement fully mediates the perfectionism-performance relationship. This demonstrates that striving for excellence alone is sufficient to achieve positive performance, challenging the traditional belief that one must focus on perfection. Furthermore, perfectionism is positively associated with performance only when employees are engaged and have positive motivation toward work.

Ching Ming Samuel Lau, Weiqi Wang, Haochen Shi et al.

Knowledge Editing (KE) aims to correct and update factual information in Large Language Models (LLMs) to ensure accuracy and relevance without computationally expensive fine-tuning. Though it has been proven effective in several domains, limited work has focused on its application within the e-commerce sector. However, there are naturally occurring scenarios that make KE necessary in this domain, such as the timely updating of product features and trending purchase intentions by customers, which necessitate further exploration. In this paper, we pioneer the application of KE in the e-commerce domain by presenting ECOMEDIT, an automated e-commerce knowledge editing framework tailored for e-commerce-related knowledge and tasks. Our framework leverages more powerful LLMs as judges to enable automatic knowledge conflict detection and incorporates conceptualization to enhance the semantic coverage of the knowledge to be edited. Through extensive experiments, we demonstrate the effectiveness of ECOMEDIT in improving LLMs' understanding of product descriptions and purchase intentions. We also show that LLMs, after our editing, can achieve stronger performance on downstream e-commerce tasks.

Cong Xu, Yunhang He, Jun Wang et al.

While the mining of modalities is the focus of most multimodal recommendation methods, we believe that how to fully utilize both collaborative and multimodal information is pivotal in e-commerce scenarios where, as clarified in this work, the user behaviors are rarely determined entirely by multimodal features. In order to combine the two distinct types of information, some additional challenges are encountered: 1) Modality erasure: Vanilla graph convolution, which proves rather useful in collaborative filtering, however erases multimodal information; 2) Modality forgetting: Multimodal information tends to be gradually forgotten as the recommendation loss essentially facilitates the learning of collaborative information. To this end, we propose a novel approach named STAIR, which employs a novel STepwise grAph convolution to enable a co-existence of collaborative and multimodal Information in e-commerce Recommendation. Besides, it starts with the raw multimodal features as an initialization, and the forgetting problem can be significantly alleviated through constrained embedding updates. As a result, STAIR achieves state-of-the-art recommendation performance on three public e-commerce datasets with minimal computational and memory costs. Our code is available at https://github.com/yhhe2004/STAIR.

Yafei Xiang, Hanyi Yu, Yulu Gong et al.

With the rapid development of artificial intelligence technology, Transformer structural pre-training model has become an important tool for large language model (LLM) tasks. In the field of e-commerce, these models are especially widely used, from text understanding to generating recommendation systems, which provide powerful technical support for improving user experience and optimizing service processes. This paper reviews the core application scenarios of Transformer pre-training model in e-commerce text understanding and recommendation generation, including but not limited to automatic generation of product descriptions, sentiment analysis of user comments, construction of personalized recommendation system and automated processing of customer service conversations. Through a detailed analysis of the model's working principle, implementation process, and application effects in specific cases, this paper emphasizes the unique advantages of pre-trained models in understanding complex user intentions and improving the quality of recommendations. In addition, the challenges and improvement directions for the future are also discussed, such as how to further improve the generalization ability of the model, the ability to handle large-scale data sets, and technical strategies to protect user privacy. Ultimately, the paper points out that the application of Transformer structural pre-training models in e-commerce has not only driven technological innovation, but also brought substantial benefits to merchants and consumers, and looking forward, these models will continue to play a key role in e-commerce and beyond.

Reza Barzegar Nozari, Mahdi Divsalar, Sepehr Akbarzadeh Abkenar et al.

The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining neighborhoods, collecting similar users, calculating product reputation based on similar users, and recommending high-reputation products. To overcome the complexity of customer behaviors and traditional clustering methods, an unsupervised clustering approach based on product categories is developed to enhance the recommendation methodology. This study makes notable contributions in several aspects. Firstly, a groundbreaking behavior-based recommendation methodology is developed, incorporating customer behavior to generate accurate and tailored recommendations leading to improved customer satisfaction and engagement. Secondly, an original unsupervised clustering method, focusing on product categories, enables more precise clustering and facilitates accurate recommendations. Finally, an approach to determine neighborhoods for active customers within clusters is established, ensuring grouping of customers with similar behavioral patterns to enhance recommendation accuracy and relevance. The proposed recommendation methodology and clustering method contribute to improved recommendation performance, offering valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Additionally, the proposed method outperforms benchmark methods in experiments conducted using a behavior dataset from the well-known e-commerce site Alibaba.

Yingnan Zhang, H. Long, Li Ma et al.

Abstract In recent years, China's rural areas have undergone intense restructuring motivated by various flows derived from e-commerce, which has triggered a new wave of rural rejuvenation. Attempting to reveal the process and mechanism of rural economic restructuring driven by e-commerce, this paper takes Xiaying village in central China to conduct an empirical study by introducing the theory of “space of flows” and applying the method of semi-structured interview. The results show that e-commerce has become a technical catalyst to the variation of industry structure, employment pattern and household economy. Xiaying village has performed a leap from traditional agriculture to commercial service and constructed a complete e-commerce oriented industry chain, which is distinguished from the traditional path of rural modernization adhering to the gradual evolution of the primary, secondary and tertiary industry in China. In response, the employment pattern is diversified and tends to be de-agriculturalization, thus providing an economic advancement opportunity for rural households. As a matter of fact, rural elites, technology innovation (e-commerce platform), resource endowments and government support all contributed greatly to this restructuring process. What distinguishes it from others is the strong mobility and exchange of urban and rural elements, which functions as the initial engine. Essentially, this transition can be considered as the impact of the network on the geographic space restructuring rural economy.

E. Turban, Jon Outland, David King et al.

Kaiquan Xu, Jason Chan, A. Ghose et al.

Chia-Ying Li

Abstract Social commerce takes advantage of social networking capabilities and provides features that encourage customers to share personal experiences. Because social commerce has evolved quickly, it has not been studied as extensively as e-commerce and social networking. Extant research on social commerce has revealed only a few of its characteristics. Applying a stimulus–organism–response model, this study investigated the influences of social commerce sites on customers' virtual experiences and on their intentions to purchase products. The findings indicated that social commerce constructs exerted positive and significant effects on social interactions in terms of cognitive states (social presence, informational support, and emotional support) and affective states (familiarity and closeness), but they did not exert significant influences on social shopping intention. Furthermore, social presence and informational support influenced intention to trust in product recommendations. However, neither emotional support nor familiarity exerted significant influence on trust in product recommendations. Moreover, familiarity was significantly affected by informational support and emotional support but not by social presence. Finally, familiarity exerted a positive and significant effect on closeness, which further exerted a positive and significant effect on trust in product recommendations. Trust in product recommendations exerted a significant effect on social shopping intention, whereas informational support did not. These findings explain how customers use social networking sites to aid purchasing decisions and assist online vendors in developing advertising and promotion strategies.

J. Oláh, Nicodemus Kitukutha, Hossam Haddad et al.

The Internet revolution has led to the advancement of online business all over the world. The environmental, social, and economic aspects are significant to the e-commerce sector, on both the retailer and consumer sides. It cannot be over-emphasized how important the sustainability of e-commerce in all three dimensions is. E-commerce will allow consumers to shop online easily, at any hour of the day, using secure payment systems; furthermore, trust in retailers’ websites is of paramount importance to consumers. This calls our attention to the gap in previous studies, and consequently, the purpose of this study is to fill the gap, to ensure sustainable e-commerce in three dimensions; environmental, social, and economic. The question and aim under investigation are: How to integrate three dimensions into e-commerce to ensure that sustainability is achieved now and for future generations, while thriving as an industry? Collaboration is required, and all stakeholders in the virtual market must take appropriate responsibility. The methodology adopted is a review of previous studies done on each individual dimension of sustainability, since no joint studies have been carried out and integrated into the same literature framework. Furthermore, a case study involving companies in Kenya and Jordan is used in order to collect empirical data. The findings of the study show that: First, integration is essential for the sustainability of e-commerce in its three dimensions; second, trade-offs must be taken in the various dimensions in order for companies to realize sustainable e-commerce. This will go in hand with the realization of the maximum benefits of integrating the three dimensions in e-commerce to make it more sustainable. In conclusion, by applying these aspects of sustainability in e-commerce, it is clear that everyone wins. This is achieved by improving and safeguarding the quality of life by protecting the environment, preserving natural resources, and maintaining and sustaining the economy. The implications of the study are that, in order to make e-commerce more sustainable, to make decisions and take action, social/environmental/economic aspects must be considered as a fundamental element, and must be treated as a group and not separately as in previous studies. In this way, we can realize greater benefits, not only in online business sustainability, but also in policy-making and environmental protection, while companies will create economic value as well as avoiding labor unrest.

Mahdi Choshin, A. Ghaffari

ILIE (MARIN) NICOLETA, TODERASC STEFAN ALIN, OPREA IULIA ALEXANDRA

Digital transformation represents one of the most current points of interest for the European Union. However, in agriculture, is digitalization a reality of the present or just a vision for the future? More and more processes are integrating into modern agriculture as offline and online activities increasingly converge in today's transition to digital agriculture. As the entire process is continuously developing, real opportunities emerge for all countries. A resilient agriculture that offers a secure future, based on minimal resources and sustainability, can only be built through a common concentration of efforts. Digital technologies have the potential to revolutionize agriculture and help farmers work more precisely, efficiently, and sustainably. Perspectives created from concrete data can improve decision-making processes and performance in favor of the environment, making the job itself more attractive to the new generations of farmers. Digital technologies also provide increased transparency for end consumers throughout the distribution chain. The digitalization of Romanian agriculture can represent a turning point towards development at its true capacity, even in the current small-scale context. This paper aims to outline the opportunities and limitations that may arise on the path towards the digitalization of Romanian agriculture.