Hasil untuk "Commerce"

M. Nilashi, Karamollah Bagherifard, O. Ibrahim et al.

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.

Ming-Chi Lee

P. Pavlou, Huigang Liang, Yajiong Xue

M. Franklin, M. Reiter

Matthew K. O. Lee, E. Turban

Zhiguo Chen, Guohao Sun, Yiming Qiu et al.

Generative Retrieval (GR) has emerged as a powerful paradigm in e-commerce search, retrieving items via autoregressive decoding of Semantic IDs (SIDs). However, aligning GR with complex user preferences remains challenging. While Direct Preference Optimization (DPO) offers an efficient alignment solution, its direct application to structured SIDs suffers from three limitations: (i) it penalizes shared hierarchical prefixes, causing gradient conflicts; (ii) it is vulnerable to noisy pseudo-negatives from implicit feedback; and (iii) in multi-label queries with multiple relevant items, it exacerbates a probability "squeezing effect" among valid candidates. To address these issues, we propose RAD-DPO, which introduces token-level gradient detachment to protect prefix structures, similarity-based dynamic reward weighting to mitigate label noise, and a multi-label global contrastive objective integrated with global SFT loss to explicitly expand positive coverage. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform demonstrate significant improvements in ranking quality and training efficiency.

Emad Siddiqui, Venkatesh Terikuti, Xuan Lu

Conversational user queries are increasingly challenging traditional e-commerce platforms, whose search systems are typically optimized for keyword-based queries. We present an LLM-based semantic search framework that effectively captures user intent from conversational queries by combining domain-specific embeddings with structured filters. To address the challenge of limited labeled data, we generate synthetic data using LLMs to guide the fine-tuning of two models: an embedding model that positions semantically similar products close together in the representation space, and a generative model for converting natural language queries into structured constraints. By combining similarity-based retrieval with constraint-based filtering, our framework achieves strong precision and recall across various settings compared to baseline approaches on a real-world dataset.

Yuehan Qiao, Zhihao Yao, Meiyu Hu et al.

Deaf and Hard-of-Hearing (DHH) individuals are increasingly participating as livestreamers in China's e-commerce livestreaming industry but face obstacles that limit the scope and diversity of their audience. Our paper examines these challenges and explores a potential solution for connecting the hearing audience to sign language (SL) livestreaming teams with DHH members in e-commerce livestreaming. We interviewed four SL livestreaming team members and 15 hearing audience members to identify information and emotional communication challenges that discourage the hearing audience from continuing to watch SL livestreaming. Based on these findings, we developed a virtual co-presenter demo, which targets SL livestreaming teams with DHH members as users, through a design workshop with six designers, incorporating voice broadcasting with animations. Follow-up evaluations with previous participants provided positive feedback on the virtual co-presenter's potential to address these challenges. We summarize design suggestions on its functionality and interaction design for further refinement to assist SL livestreaming teams with DHH members in reaching a broader hearing audience.

Waaridh Borpujari

The use of animation to gain user attention has been increasing, supported by various studies on user behavior and psychology. However, excessive use of animation in interfaces can negatively impact the user. This paper deals with a specific type of animation within a specialized domain of e-commerce. Drawing upon theories such as the Zeigarnik Effect, Aesthetic-Usability effect, Peak-End rule, and Hick's law, we analyze user behavior and psychology when exposed to a dynamic price-drop animation. Unlike conventional static pricing strategy, this animation introduces movement to signify price reduction. In our theoretical study approach, we evaluate and present a user study on how such an animation influences user perception, psychology, and attention. If acquired effectively, dynamic animations can enhance engagement, spark anticipation, and subconsciously create a positive experience by reducing cognitive load.

Fatimo Adenike Adeniya

Cyberattacks on e-commerce platforms have grown in sophistication, threatening consumer trust and operational continuity. This research presents a hybrid analytical framework that integrates statistical modelling and machine learning for detecting and forecasting cyberattack patterns in the e-commerce domain. Using the Verizon Community Data Breach (VCDB) dataset, the study applies Auto ARIMA for temporal forecasting and significance testing, including a Mann-Whitney U test (U = 2579981.5, p = 0.0121), which confirmed that holiday shopping events experienced significantly more severe cyberattacks than non-holiday periods. ANOVA was also used to examine seasonal variation in threat severity, while ensemble machine learning models (XGBoost, LightGBM, and CatBoost) were employed for predictive classification. Results reveal recurrent attack spikes during high-risk periods such as Black Friday and holiday seasons, with breaches involving Personally Identifiable Information (PII) exhibiting elevated threat indicators. Among the models, CatBoost achieved the highest performance (accuracy = 85.29%, F1 score = 0.2254, ROC AUC = 0.8247). The framework uniquely combines seasonal forecasting with interpretable ensemble learning, enabling temporal risk anticipation and breach-type classification. Ethical considerations, including responsible use of sensitive data and bias assessment, were incorporated. Despite class imbalance and reliance on historical data, the study provides insights for proactive cybersecurity resource allocation and outlines directions for future real-time threat detection research.

Yimin Shi, Yang Fei, Shiqi Zhang et al.

In e-commerce, user representations are essential for various applications. Existing methods often use deep learning techniques to convert customer behaviors into implicit embeddings. However, these embeddings are difficult to understand and integrate with external knowledge, limiting the effectiveness of applications such as customer segmentation, search navigation, and product recommendations. To address this, our paper introduces the concept of the customer persona. Condensed from a customer's numerous purchasing histories, a customer persona provides a multi-faceted and human-readable characterization of specific purchase behaviors and preferences, such as Busy Parents or Bargain Hunters. This work then focuses on representing each customer by multiple personas from a predefined set, achieving readable and informative explicit user representations. To this end, we propose an effective and efficient solution GPLR. To ensure effectiveness, GPLR leverages pre-trained LLMs to infer personas for customers. To reduce overhead, GPLR applies LLM-based labeling to only a fraction of users and utilizes a random walk technique to predict personas for the remaining customers. We further propose RevAff, which provides an absolute error $ε$ guarantee while improving the time complexity of the exact solution by a factor of at least $O(\frac{ε\cdot|E|N}{|E|+N\log N})$, where $N$ represents the number of customers and products, and $E$ represents the interactions between them. We evaluate the performance of our persona-based representation in terms of accuracy and robustness for recommendation and customer segmentation tasks using three real-world e-commerce datasets. Most notably, we find that integrating customer persona representations improves the state-of-the-art graph convolution-based recommendation model by up to 12% in terms of NDCG@K and F1-Score@K.

Urvashi Kishnani, Sanchari Das

As e-commerce continues to expand, the urgency for stronger privacy and security measures becomes increasingly critical, particularly on platforms frequented by younger users who are often less aware of potential risks. In our analysis of 90 US-based e-commerce websites, we employed a dual-technique approach, combining automated tools with manual evaluations. Tools like CookieServe and PrivacyCheck revealed that 38.5% of the websites deployed over 50 cookies per session, many of which were categorized as unnecessary or unclear in function, posing significant risks to users' Personally Identifiable Information (PII). Our manual assessment further uncovered critical gaps in standard security practices, including the absence of mandatory multi-factor authentication (MFA) and breach notification protocols. Additionally, we observed inadequate input validation, which compromises the integrity of user data and transactions. Based on these findings, we recommend targeted improvements to privacy policies, enhanced transparency in cookie usage, and the implementation of stronger authentication protocols. These measures are essential for ensuring compliance with CCPA and COPPA, thereby fostering more secure online environments, particularly for younger users.

Enqiang Xu, Xinhui Li, Zhigong Zhou et al.

In the rapidly evolving field of e-commerce, the effectiveness of search re-ranking models is crucial for enhancing user experience and driving conversion rates. Despite significant advancements in feature representation and model architecture, the integration of multimodal information remains underexplored. This study addresses this gap by investigating the computation and fusion of textual and visual information in the context of re-ranking. We propose \textbf{A}dvancing \textbf{R}e-Ranking with \textbf{M}ulti\textbf{m}odal Fusion and \textbf{T}arget-Oriented Auxiliary Tasks (ARMMT), which integrates an attention-based multimodal fusion technique and an auxiliary ranking-aligned task to enhance item representation and improve targeting capabilities. This method not only enriches the understanding of product attributes but also enables more precise and personalized recommendations. Experimental evaluations on JD.com's search platform demonstrate that ARMMT achieves state-of-the-art performance in multimodal information integration, evidenced by a 0.22\% increase in the Conversion Rate (CVR), significantly contributing to Gross Merchandise Volume (GMV). This pioneering approach has the potential to revolutionize e-commerce re-ranking, leading to elevated user satisfaction and business growth.

Huimu Wang, Mingming Li, Dadong Miao et al.

Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversity, leading to outcomes that may not fulfill the varied needs of users. Conversely, methods designed to promote diversity might compromise the precision of the results, failing to satisfy the users' requirements for accuracy. To alleviate the above problems, this paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI), which consider both accuracy and diversity in the re-ranking process. Specifically, PODM-MI adopts Multidimensional Gaussian distributions based on variational inference to capture users' diversity preferences with uncertainty. Then we maximize the mutual information between the diversity preferences of the users and the candidate items using the maximum variational inference lower bound to enhance their correlations. Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of PODM-MI, and we have successfully deployed PODM-MI on an e-commerce search platform.

Anant Khandelwal, Happy Mittal, Shreyas Sunil Kulkarni et al.

E-commerce websites (e.g. Amazon) have a plethora of structured and unstructured information (text and images) present on the product pages. Sellers often either don't label or mislabel values of the attributes (e.g. color, size etc.) for their products. Automatically identifying these attribute values from an eCommerce product page that contains both text and images is a challenging task, especially when the attribute value is not explicitly mentioned in the catalog. In this paper, we present a scalable solution for this problem where we pose attribute extraction problem as a question-answering task, which we solve using \textbf{MXT}, consisting of three key components: (i) \textbf{M}AG (Multimodal Adaptation Gate), (ii) \textbf{X}ception network, and (iii) \textbf{T}5 encoder-decoder. Our system consists of a generative model that \emph{generates} attribute-values for a given product by using both textual and visual characteristics (e.g. images) of the product. We show that our system is capable of handling zero-shot attribute prediction (when attribute value is not seen in training data) and value-absent prediction (when attribute value is not mentioned in the text) which are missing in traditional classification-based and NER-based models respectively. We have trained our models using distant supervision, removing dependency on human labeling, thus making them practical for real-world applications. With this framework, we are able to train a single model for 1000s of (product-type, attribute) pairs, thus reducing the overhead of training and maintaining separate models. Extensive experiments on two real world datasets show that our framework improves the absolute recall@90P by 10.16\% and 6.9\% from the existing state of the art models. In a popular e-commerce store, we have deployed our models for 1000s of (product-type, attribute) pairs.

Ольга Гірна

У статті розкрито сутність концепції Кайдзен. Першочерговий аспект зосереджено на теоретичній базі, зокрема, підкреслено важливість безперервного вдосконалення процесів виробництва, розробки допоміжних бізнес-процесів, управління ними. Представлено взаємозв’язок Кайдзену з теорією загального управління якістю (ТQM), що дозволяє виокремити три основні напрями функціонування з орієнтацією на якість. Розкрито практичні аспекти реалізації Lean Production (LP) та Just-in-time (JIT) на основі Кайдзену. Пріоритетну роль в дослідженні відведено Інституту Кайдзену, який зосереджує свою увагу на навчанні, сертифікації та бенчмаркінгу у напрямі Кайдзен. Підкреслено важливість розроблення моделі TFL, яка дає можливість покращити процеси в цілому ланцюгу постачання. Окреслено нові прогресивні напрями розвитку даної концепції (Гемба Кайдзен, Бліц Кайдзен).

Lianghai Xiao, Yixing Zhao, Jiwei Chen

The online advertising management platform has become increasingly popular among e-commerce vendors/advertisers, offering a streamlined approach to reach target customers. Despite its advantages, configuring advertising strategies correctly remains a challenge for online vendors, particularly those with limited resources. Ineffective strategies often result in a surge of unproductive ``just looking'' clicks, leading to disproportionately high advertising expenses comparing to the growth of sales. In this paper, we present a novel profit-maximing strategy for targeting options of online advertising. The proposed model aims to find the optimal set of features to maximize the probability of converting targeted audiences into actual buyers. We address the optimization challenge by reformulating it as a multiple-choice knapsack problem (MCKP). We conduct an empirical study featuring real-world data from Tmall to show that our proposed method can effectively optimize the advertising strategy with budgetary constraints.

Mohammadreza Ebrahimi, Yidong Chai, Hao Helen Zhang et al.

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.

Mario Almagro, David Jiménez, Diego Ortego et al.

Product matching is a fundamental step for the global understanding of consumer behavior in e-commerce. In practice, product matching refers to the task of deciding if two product offers from different data sources (e.g. retailers) represent the same product. Standard pipelines use a previous stage called blocking, where for a given product offer a set of potential matching candidates are retrieved based on similar characteristics (e.g. same brand, category, flavor, etc.). From these similar product candidates, those that are not a match can be considered hard negatives. We present Block-SCL, a strategy that uses the blocking output to make the most of Supervised Contrastive Learning (SCL). Concretely, Block-SCL builds enriched batches using the hard-negatives samples obtained in the blocking stage. These batches provide a strong training signal leading the model to learn more meaningful sentence embeddings for product matching. Experimental results in several public datasets demonstrate that Block-SCL achieves state-of-the-art results despite only using short product titles as input, no data augmentation, and a lighter transformer backbone than competing methods.

Hommane Boudine, Meriem Bentaleb, Mourad Radi et al.

In early 2020 new pedagogical practices and approaches were adopted to ensure the continuity of the education system during the health crisis caused by the coronavirus pandemic (covid19) to make the student more active in the new learning process. This study focuses on manipulating information and communication technologies in education (ICT) to support the pedagogical alternation between distance and face-to-face education to ensure equity and equal opportunities. The objective of this study is to assess the implementation of reverse pedagogy and the obstacles that hinder it by focusing on this new process that offers the learner the opportunity to see the course support through the digital tool, using an educational platform (Moodle), which gives each student the opportunity to learn and evolve at their own pace, without losing the motivation to learn. This research is addressed to students of the last year of the college cycle at the ALYASSAMIN school in Sidi Slimane. The results obtained confirm that the pedagogical model of the flipped classroom ensured a stable progression of the education system during the period of the health crisis. This study is based on assessing the state of play and suggests the generalization of inverted classes and integrating them into teaching practices. In conclusion, the results presented must be considered for adapted teaching to support alternative education and to follow the current digital evolution.