A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Personalizes query completion in digital commerce with image-based vectors.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
This paper discusses e-commerce integration with SAP for Turkish businesses.
This study synthesizes stablecoin systems and develops a performance evaluation framework.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
SessionPath improves category suggestions in type-ahead search.
We evaluate how modern outlier detection methods perform in identifying outliers in e-commerce conversion rate data. Based on the limitations identified, we then present a novel method to detect outliers in e-commerce conversion rate. This unsupervised method is made more business relevant by letting it automatically a…
Sales forecasts are crucial for the E-commerce business. State-of-the-art techniques typically apply only univariate methods to make prediction for each series independently. However, due to the short nature of sales times series in E-commerce, univariate methods don't apply well. In this article, we propose a global m…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
As Internet-based commerce becomes increasingly widespread, large data sets about the demand for and pricing of a wide variety of products become available. These present exciting new opportunities for empirical economic and business research, but also raise new statistical issues and challenges. In this article, we su…
RL platform enhances user journeys in healthcare apps.
The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.
Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.
In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers' engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, tr…
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertiseme…
InfDetect detects e-commerce insurance fraud using graph analysis.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
The paper proposes a machine learning technique to optimize prices in fashion e-commerce.
E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad retrieval and ad pr…
Analyzes stock trends and e-commerce user behavior using Twitter data.
Paper proposes a deep learning model for understanding e-commerce addresses.
A deep reinforcement learning approach for slate re-ranking in e-commerce.
In many developing countries intellectual property infringement and the commerce of pirate goods is an entrepreneurial activity. Digital piracy is very often the only media for having access to music, cinema, books and software. At the same time, bio-prospecting and infringement of indigenous knowledge rights by intern…
Automatic voice-controlled systems have changed the way humans interact with a computer. Voice or speech recognition systems allow a user to make a hands-free request to the computer, which in turn processes the request and serves the user with appropriate responses. After years of research and developments in machine …
Search is a prominent channel for discovering products on an e-commerce platform. Ranking products retrieved from search becomes crucial to address customer's need and optimize for business metrics. While learning to Rank (LETOR) models have been extensively studied and have demonstrated efficacy in the context of web …
Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conver…
Optimizes package types for e-commerce to reduce damage and costs.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
New bandit model for e-commerce with ordered categories.
TXtract extracts structured knowledge from thousands of product categories.
On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if querie…
Study uses generative models to assess credit risk and determine loan sizes in e-commerce supply chain finance.
Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing. Of particular significance is the book category, where the author name(s) field poses a significant challenge. Indeed, books writ…
In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform. Existing methods mainly focus on the short-term return based on the current observations instead of the long-term return. In this paper, we formally establish the lifecycle model for products, by…
KFAtt improves CTR prediction by modeling user behavior with Kalman filtering attention.
Digital money could reduce germ spread during coronavirus.
Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…
With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of prod…
New approach improves stock policies for paper companies, reducing waste and costs.
In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable …
Study of digital topology concepts like hyperspaces and function graphs.
Proposes a novel network for CTR prediction by learning modality-specific and modality-invariant representations.
The paper highlights issues with fixed point claims in digital images.
Find limiting sets for digital cones and suspensions.
In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…
Corrects incorrect assertions about fixed points in digital topology.
Study AFPP of unions of convex digital disks in 2D.
The paper addresses flaws in fixed point assertions for digital images.