Hasil untuk "Commerce"

A. Shaikh, Heikki Karjaluoto

R. Anderson, S. Srinivasan

Matthew K. O. Lee, E. Turban

Jim Hendler

Why is Google so good at what it does? There ate a variety of reasons, but the fundamental thing that distinguishes Google and has put them so far ahead of other search engines is their patented PageRank concept. PageRank has revolutionized Web search to the extent that it has been charged in Federal Court with driving the direction of commerce on the Internet. Many mathematicians are therefore surprised when they learn that a technology of such consequence is predicated on the same mathematics that is available to undergraduate students. This talk will survey some of these concepts.

Roslan Ismail, A. Jøsang

R. Guha, Ravi Kumar, Prabhakar Raghavan et al.

Jessica Santos

D. Hoffman, T. Novak, M. Peralta

Gunther Eysenbach

Everybody talks about e-health these days, but few people have come up with a clear definition of this comparatively new term. Barely in use before 1999, this term now seems to serve as a general "buzzword," used to characterize not only "Internet medicine", but also virtually everything related to computers and medicine. The term was apparently first used by industry leaders and marketing people rather than academics. They created and used this term in line with other "e-words" such as e-commerce, e-business, e-solutions, and so on, in an attempt to convey the promises, principles, excitement (and hype) around e-commerce (electronic commerce) to the health arena, and to give an account of the new possibilities the Internet is opening up to the area of health care. Intel, for example, referred to e-health as "a concerted effort undertaken by leaders in health care and hi-tech industries to fully harness the benefits available through convergence of the Internet and health care." Because the Internet created new opportunities and challenges to the traditional health care information technology industry, the use of a new term to address these issues seemed appropriate. These "new" challenges for the health care information technology industry were mainly (1) the capability of consumers to interact with their systems online (B2C = "business to consumer"); (2) improved possibilities for institutionto-institution transmissions of data (B2B = "business to business"); (3) new possibilities for peerto-peer communication of consumers (C2C = "consumer to consumer").

Junxian Wu, Chenghan Fu, Zhanheng Nie et al.

With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language models (MLLMs) have driven significant progress in product understanding, they are typically employed as feature extractors that implicitly encode product information into global embeddings, thereby limiting their ability to capture fine-grained attributes. Therefore, we argue that leveraging the reasoning capabilities of MLLMs to explicitly model fine-grained product attributes holds significant potential. Nevertheless, achieving this goal remains non-trivial due to several key challenges: (i) long-context reasoning tends to dilute the model's attention to salient information in the raw input; (ii) supervised fine-tuning (SFT) primarily encourages rigid imitation, limiting the exploration of effective reasoning strategies; and (iii) fine-grained details are progressively attenuated during forward propagation. To address these issues, we propose MOON3.0, the first reasoning-aware MLLM-based model for product representation learning. Our method (1) employs a multi-head modality fusion module to adaptively integrate raw signals; (2) incorporates a joint contrastive and reinforcement learning framework to autonomously explore more effective reasoning strategies; and (3) introduces a fine-grained residual enhancement module to progressively preserve local details throughout the network. Additionally, we release a large-scale multimodal e-commerce benchmark MBE3.0. Experimentally, our model demonstrates state-of-the-art zero-shot performance across various downstream tasks on both our benchmark and public datasets.

Baopu Qiu, Hao Chen, Yuanrong Wu et al.

Effective relevance modeling is crucial for e-commerce search, as it aligns search results with user intent and enhances customer experience. Recent work has leveraged large language models (LLMs) to address the limitations of traditional relevance models, especially for long-tail and ambiguous queries. By incorporating Chain-of-Thought (CoT) reasoning, these approaches improve both accuracy and interpretability through multi-step reasoning. However, two key limitations remain: (1) most existing approaches rely on single-perspective CoT reasoning, which fails to capture the multifaceted nature of e-commerce relevance (e.g., user intent vs. attribute-level matching vs. business-specific rules); and (2) although CoT-enhanced LLM's offer rich reasoning capabilities, their high inference latency necessitates knowledge distillation for real-time deployment, yet current distillation methods discard the CoT rationale structure at inference, using it as a transient auxiliary signal and forfeiting its reasoning utility. To address these challenges, we propose a novel framework that better exploits CoT semantics throughout the optimization pipeline. Specifically, the teacher model leverages Multi-Perspective CoT (MPCoT) to generate diverse rationales and combines Supervised Fine-Tuning (SFT) with Direct Preference Optimization (DPO) to construct a more robust reasoner. For distillation, we introduce Latent Reasoning Knowledge Distillation (LRKD), which endows a student model with a lightweight inference-time latent reasoning extractor, allowing efficient and low-latency internalization of the LLM's sophisticated reasoning capabilities. Evaluated in offline experiments and online A/B tests on an e-commerce search advertising platform serving tens of millions of users daily, our method delivers significant offline gains, showing clear benefits in both commercial performance and user experience.

Jiwoo Kang, Yeon-Chang Lee

Multimodal recommender systems (MMRSs) enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objectives limits both modality extensibility and task generalization. We propose E-MMKGR, a framework that constructs an e-commerce-specific Multimodal Knowledge Graph E-MMKG and learns unified item representations through GNN-based propagation and KG-oriented optimization. These representations provide a shared semantic foundation applicable to diverse tasks. Experiments on real-world Amazon datasets show improvements of up to 10.18% in Recall@10 for recommendation and up to 21.72% over vector-based retrieval for product search, demonstrating the effectiveness and extensibility of our approach.

Lotte Gross, Rebecca Walter, Nicole Zoppi et al.

This study addresses critical industrial challenges in e-commerce product categorization, namely platform heterogeneity and the structural limitations of existing taxonomies, by developing and deploying a multimodal hierarchical classification framework. Using a dataset of 271,700 products from 40 international fashion e-commerce platforms, we integrate textual features (RoBERTa), visual features (ViT), and joint vision-language representations (CLIP). We investigate fusion strategies, including early, late, and attention-based fusion within a hierarchical architecture enhanced by dynamic masking to ensure taxonomic consistency. Results show that CLIP embeddings combined via an MLP-based late-fusion strategy achieve the highest hierarchical F1 (98.59%), outperforming unimodal baselines. To address shallow or inconsistent categories, we further introduce a self-supervised "product recategorization" pipeline using SimCLR, UMAP, and cascade clustering, which discovered new, fine-grained categories (for example, subtypes of "Shoes") with cluster purities above 86%. Cross-platform experiments reveal a deployment-relevant trade-off: complex late-fusion methods maximize accuracy with diverse training data, while simpler early-fusion methods generalize more effectively to unseen platforms. Finally, we demonstrate the framework's industrial scalability through deployment in EURWEB's commercial transaction intelligence platform via a two-stage inference pipeline, combining a lightweight RoBERTa stage with a GPU-accelerated multimodal stage to balance cost and accuracy.

Junxing Hu, Ai Han, Haolan Zhan et al.

Hierarchical multi-agent systems based on large language models (LLMs) have become a common paradigm for building AI assistants in vertical domains such as e-commerce, where a master agent coordinates multiple specialized sub-agents. Despite their practical importance, realistic benchmarks for training and evaluating such systems remain scarce, and joint optimization across functionally distinct agents is still challenging. To address this gap, we introduce HiMA-Ecom, the first hierarchical multi-agent benchmark tailored for e-commerce scenarios. HiMA-Ecom contains 22.8K instances, including agent-specific supervised fine-tuning samples with memory and system-level input-output pairs for joint multi-agent reinforcement learning. Building upon it, a joint training method named HiMA-R1 is proposed. It presents Variance-Reduction Group Relative Policy Optimization (VR-GRPO), which employs initial trajectory-based Monte Carlo sampling to mitigate the exponential joint action space and selects informative agent groups for efficient updates based on reward variance. Furthermore, an adaptive memory evolution mechanism that repurposes GRPO rewards as cost-free supervisory signals is designed to eliminate repetitive reasoning and accelerate convergence. Experiments on HiMA-Ecom demonstrate that our method, built upon smaller 3B/7B open-source models, achieves performance comparable to that of larger LLMs, such as DeepSeek-R1, and surpasses DeepSeek-V3 by an average of 6\%.

Muttaka Ibrahim Hashimu, Yazid Ibrahim Kabir

Tax is an important source of revenue to any government; however, the amount of tax paid by consumer goods firms in Nigeria is sometimes low due to firms’ tax sheltering activities. The study therefore investigates how board characteristics influence tax sheltering practices among listed consumer goods firms in Nigeria. The study adopts an ex-post factor research design because historical data were utilized. The population of the study comprised all 21 consumer goods firms listed in Nigeria as at 31 December 2024. Using a purposeful sampling technique and based on data availability, the study covered 13 firms. Secondary data covering a ten-year period (2015 to 2024) were sourced from the annual reports of the sampled firms. Descriptive and regression analyses were conducted using STATA 13 statistical software. The results show that board gender diversity has positive significance effect on tax sheltering (coeff. 0.411, P-value 0.038), board meetings have a positive and statistically significant effect on tax sheltering (coeff. 0.0512, P-value 0.018), board size exhibits a negative but insignificant effect (coeff. 0.004, P-value 0.619), while board independence shows a positive insignificant effect on tax sheltering (coeff. 0.122, P-value 0.301). Based on its findings, the study concludes that board characteristics exert a meaningful influence on tax sheltering practices among listed consumer goods firms in Nigeria. Based on its conclusion, the study recommends that listed consumer goods firms in Nigeria should complement increased female board representation and frequent board meetings with robust board-level tax governance, including clear tax planning limits, periodic compliance reviews, and transparent documentation of tax decisions, while regulators strengthen monitoring and enforcement of corporate reporting practices.

Wilmark Ramos

Type of the article: Research Article AbstractThe rapid growth of social media has transformed consumer-brand interactions, making the study of attachment and consumer engagement highly relevant for emerging markets such as the Philippines. This research aims to examine how higher-order constructs of attachment (identity-based attachment and bonding-based attachment) influence consumer engagement with local brands on Facebook and, subsequently, how this engagement shapes purchase intention. To achieve this, a structured online survey was conducted among 386 Filipino Facebook users who actively followed at least one local brand page. Data collection took place between August and October 2024, employing purposive sampling to ensure the representativeness of engaged local brand consumers. The survey method was chosen to capture consumers’ psychological attachment and behavioural responses within an authentic digital setting. Using Structural Equation Modelling (SEM), the findings reveal that bonding-based attachment exerts the strongest influence on consumer engagement (β = 0.56, p < 0.01), while identity-based attachment also demonstrates a significant but comparatively weaker effect (β = 0.34, p < 0.01). Both attachment dimensions indirectly enhance purchase intention through consumer engagement (β = 0.302 for bonding-based attachment, and β = 0.186 for identity-based attachment), with consumer engagement itself emerging as a robust predictor of purchase intention (β = 0.542, p < 0.001). Demographic characteristics such as age, gender, and income showed no significant moderating effects on the consumer engagement-purchase intention relationship. These results provide empirical support for the mediating role of consumer engagement in attachment-intention pathways, extending attachment theory into social media marketing and offering practical insights for brand managers in emerging economies.

Jingyu Liu, Minquan Wang, Ye Ma et al.

Videos showcasing specific products are increasingly important for E-commerce. Key moments naturally exist as the first appearance of a specific product, presentation of its distinctive features, the presence of a buying link, etc. Adding proper sound effects (SFX) to these key moments, or video decoration with SFX (VDSFX), is crucial for enhancing the user engaging experience. Previous studies about adding SFX to videos perform video to SFX matching at a holistic level, lacking the ability of adding SFX to a specific moment. Meanwhile, previous studies on video highlight detection or video moment retrieval consider only moment localization, leaving moment to SFX matching untouched. By contrast, we propose in this paper D&M, a unified method that accomplishes key moment detection and moment to SFX matching simultaneously. Moreover, for the new VDSFX task we build a large-scale dataset SFX-Moment from an E-commerce platform. For a fair comparison, we build competitive baselines by extending a number of current video moment detection methods to the new task. Extensive experiments on SFX-Moment show the superior performance of the proposed method over the baselines.

Nurendra Choudhary, Edward W Huang, Karthik Subbian et al.

The problem of search relevance in the E-commerce domain is a challenging one since it involves understanding the intent of a user's short nuanced query and matching it with the appropriate products in the catalog. This problem has traditionally been addressed using language models (LMs) and graph neural networks (GNNs) to capture semantic and inter-product behavior signals, respectively. However, the rapid development of new architectures has created a gap between research and the practical adoption of these techniques. Evaluating the generalizability of these models for deployment requires extensive experimentation on complex, real-world datasets, which can be non-trivial and expensive. Furthermore, such models often operate on latent space representations that are incomprehensible to humans, making it difficult to evaluate and compare the effectiveness of different models. This lack of interpretability hinders the development and adoption of new techniques in the field. To bridge this gap, we propose Plug and Play Graph LAnguage Model (PP-GLAM), an explainable ensemble of plug and play models. Our approach uses a modular framework with uniform data processing pipelines. It employs additive explanation metrics to independently decide whether to include (i) language model candidates, (ii) GNN model candidates, and (iii) inter-product behavioral signals. For the task of search relevance, we show that PP-GLAM outperforms several state-of-the-art baselines as well as a proprietary model on real-world multilingual, multi-regional e-commerce datasets. To promote better model comprehensibility and adoption, we also provide an analysis of the explainability and computational complexity of our model. We also provide the public codebase and provide a deployment strategy for practical implementation.

Kento Kawaharazuka, Shintaro Inoue, Temma Suzuki et al.

Quadruped robots that individual researchers can build by themselves are crucial for expanding the scope of research due to their high scalability and customizability. These robots must be easily ordered and assembled through e-commerce or DIY methods, have a low number of components for easy maintenance, and possess durability to withstand experiments in diverse environments. Various quadruped robots have been developed so far, but most robots that can be built by research institutions are relatively small and made of plastic using 3D printers. These robots cannot withstand experiments in external environments such as mountain trails or rubble, and they will easily break with intense movements. Although there is the advantage of being able to print parts by yourself, the large number of components makes replacing broken parts and maintenance very cumbersome. Therefore, in this study, we develop a metal quadruped robot MEVIUS, that can be constructed and assembled using only materials ordered through e-commerce. We have considered the minimum set of components required for a quadruped robot, employing metal machining, sheet metal welding, and off-the-shelf components only. Also, we have achieved a simple circuit and software configuration. Considering the communication delay due to its simple configuration, we experimentally demonstrate that MEVIUS, utilizing reinforcement learning and Sim2Real, can traverse diverse rough terrains and withstand outside experiments. All hardware and software components can be obtained from https://github.com/haraduka/mevius.

Zhe Lin, Jiwei Tan, Dan Ou et al.

Text relevance or text matching of query and product is an essential technique for the e-commerce search system to ensure that the displayed products can match the intent of the query. Many studies focus on improving the performance of the relevance model in search system. Recently, pre-trained language models like BERT have achieved promising performance on the text relevance task. While these models perform well on the offline test dataset, there are still obstacles to deploy the pre-trained language model to the online system as their high latency. The two-tower model is extensively employed in industrial scenarios, owing to its ability to harmonize performance with computational efficiency. Regrettably, such models present an opaque ``black box'' nature, which prevents developers from making special optimizations. In this paper, we raise deep Bag-of-Words (DeepBoW) model, an efficient and interpretable relevance architecture for Chinese e-commerce. Our approach proposes to encode the query and the product into the sparse BoW representation, which is a set of word-weight pairs. The weight means the important or the relevant score between the corresponding word and the raw text. The relevance score is measured by the accumulation of the matched word between the sparse BoW representation of the query and the product. Compared to popular dense distributed representation that usually suffers from the drawback of black-box, the most advantage of the proposed representation model is highly explainable and interventionable, which is a superior advantage to the deployment and operation of online search engines. Moreover, the online efficiency of the proposed model is even better than the most efficient inner product form of dense representation ...