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Found 8 papers, 3 papers with code
General2Specialized LLMs Translation for E-commerce
Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and legal documents.
On Practical Diversified Recommendation with Controllable Category Diversity Framework
These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects. Therefore, in this paper, we first define diversity as two distinct definitions, i. e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors.
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e. g., Alibaba. com and Amazon.
EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising
We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising.
Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval
Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images.
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
(2) It is difficult to obtain users' explicit feedback of their preference in product features.
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
