AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce
- URL: http://arxiv.org/abs/2109.07411v1
- Date: Mon, 13 Sep 2021 06:14:30 GMT
- Title: AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce
- Authors: Guohai Xu, Hehong Chen, Feng-Lin Li, Fu Sun, Yunzhou Shi, Zhixiong
Zeng, Wei Zhou, Zhongzhou Zhao, Ji Zhang
- Abstract summary: AliMe MKG is a multi-modal knowledge graph that aims at providing a cognitive profile for products.
Based on the MKG, we build an online live assistant that highlights product search, product exhibition and question answering.
Our system has been launched online in Taobao app, and currently serves hundreds of thousands of customers per day.
- Score: 8.170860497449508
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Live streaming is becoming an increasingly popular trend of sales in
E-commerce. The core of live-streaming sales is to encourage customers to
purchase in an online broadcasting room. To enable customers to better
understand a product without jumping out, we propose AliMe MKG, a multi-modal
knowledge graph that aims at providing a cognitive profile for products,
through which customers are able to seek information about and understand a
product. Based on the MKG, we build an online live assistant that highlights
product search, product exhibition and question answering, allowing customers
to skim over item list, view item details, and ask item-related questions. Our
system has been launched online in the Taobao app, and currently serves
hundreds of thousands of customers per day.
Related papers
- A BERT based Ensemble Approach for Sentiment Classification of Customer
Reviews and its Application to Nudge Marketing in e-Commerce [2.2120851074630177]
Product reviews improve customer trust and loyalty.
Nudge marketing is a subtle way for an ecommerce company to help their customers make better decisions without hesitation.
arXiv Detail & Related papers (2023-11-16T14:18:24Z) - Learning to Personalize Recommendation based on Customers' Shopping
Intents [6.503955510722271]
We introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations.
We develop a novel technique that automatically identifies various high level goals being pursued by the Amazon customers, such as "go camping", and "preparing for a beach party"
Our solution is in a scalable fashion (in 14 languages across 21 countries)
arXiv Detail & Related papers (2023-05-09T09:06:46Z) - Finding Lookalike Customers for E-Commerce Marketing [5.2300714255564795]
We present a scalable and efficient system to expand targeted audience of marketing campaigns.
We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest.
arXiv Detail & Related papers (2023-01-09T02:18:58Z) - Multi-queue Momentum Contrast for Microvideo-Product Retrieval [57.527227171945796]
We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances.
A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval.
A discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories.
arXiv Detail & Related papers (2022-12-22T03:47:14Z) - Automatic Scene-based Topic Channel Construction System for E-Commerce [46.30140767652402]
We propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production.
This work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario.
arXiv Detail & Related papers (2022-10-06T02:29:10Z) - ItemSage: Learning Product Embeddings for Shopping Recommendations at
Pinterest [60.841761065439414]
At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases.
This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
arXiv Detail & Related papers (2022-05-24T02:28:58Z) - Towards Personalized Answer Generation in E-Commerce via
Multi-Perspective Preference Modeling [62.049330405736406]
Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant.
It is insufficient to provide the same "completely summarized" answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves.
We propose a novel multi-perspective user preference model for generating personalized answers in PQA.
arXiv Detail & Related papers (2021-12-27T07:51:49Z) - Characterization of Frequent Online Shoppers using Statistical Learning
with Sparsity [54.26540039514418]
This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
arXiv Detail & Related papers (2021-11-11T05:36:39Z) - Mining Customers' Opinions for Online Reputation Generation and
Visualization in e-Commerce Platforms [0.0]
Customer reviews represent a very rich data source from which we can extract very valuable information about different online shopping experiences.
My research goal in this thesis is to develop reputation systems that can automatically provide E-commerce customers with valuable information to support them during their online decision-making process by mining online reviews expressed in natural language.
arXiv Detail & Related papers (2021-04-05T14:46:57Z) - User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience.
We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests.
Experimental results show that our model is superior to traditional generative models.
arXiv Detail & Related papers (2021-02-16T02:08:52Z) - AliMe KG: Domain Knowledge Graph Construction and Application in
E-commerce [26.600846713016605]
AliMe KG is a domain knowledge graph that captures user problems, points of interests (POI), item information and relations thereof.
It helps to understand user needs, answer pre-sales questions and generate explanation texts.
arXiv Detail & Related papers (2020-09-24T13:40:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.