AliMe KG: Domain Knowledge Graph Construction and Application in
E-commerce
- URL: http://arxiv.org/abs/2009.11684v1
- Date: Thu, 24 Sep 2020 13:40:18 GMT
- Title: AliMe KG: Domain Knowledge Graph Construction and Application in
E-commerce
- Authors: Feng-Lin Li, Hehong Chen, Guohai Xu, Tian Qiu, Feng Ji, Ji Zhang,
Haiqing Chen
- Abstract summary: 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.
- Score: 26.600846713016605
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Pre-sales customer service is of importance to E-commerce platforms as it
contributes to optimizing customers' buying process. To better serve users, we
propose AliMe KG, a domain knowledge graph in E-commerce 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. We applied AliMe KG to several online business scenarios
such as shopping guide, question answering over properties and recommendation
reason generation, and gained positive results. In the paper, we systematically
introduce how we construct domain knowledge graph from free text, and
demonstrate its business value with several applications. Our experience shows
that mining structured knowledge from free text in vertical domain is
practicable, and can be of substantial value in industrial settings.
Related papers
- Product Information Extraction using ChatGPT [69.12244027050454]
This paper explores the potential of ChatGPT for extracting attribute/value pairs from product descriptions.
Our results show that ChatGPT achieves a performance similar to a pre-trained language model but requires much smaller amounts of training data and computation for fine-tuning.
arXiv Detail & Related papers (2023-06-23T09:30:01Z) - U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce
Conversational Recommendation [59.81301478480005]
We construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledges.
arXiv Detail & Related papers (2023-05-05T01:44:35Z) - Automatic Controllable Product Copywriting for E-Commerce [58.97059802658354]
We deploy an E-commerce Prefix-based Controllable Copywriting Generation into the JD.com e-commerce recommendation platform.
We conduct experiments to validate the effectiveness of the proposed EPCCG.
We introduce the deployed architecture which cooperates with the EPCCG into the real-time JD.com e-commerce recommendation platform.
arXiv Detail & Related papers (2022-06-21T04:18:52Z) - 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) - Knowledge Graph Embedding in E-commerce Applications: Attentive
Reasoning, Explanations, and Transferable Rules [18.63983271518707]
Reasoning tasks such as link prediction and rule induction are important for the development of Knowledge Graphs.
Knowledge Graph Embeddings (KGEs) embedding entities and relations of a KG into continuous vector spaces are proven to be efficient and robust.
But the plausibility and feasibility of applying and deploying KGEs in real-work applications has not been well-explored.
arXiv Detail & Related papers (2021-12-16T03:26:36Z) - AliMe MKG: A Multi-modal Knowledge Graph for Live-streaming E-commerce [8.170860497449508]
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.
arXiv Detail & Related papers (2021-09-13T06:14:30Z) - A Core of E-Commerce Customer Experience based on Conversational Data
using Network Text Methodology [0.0]
This paper applies to e-commerces and customers in Indonesia.
By understanding customer behavior through open social network service, we can have descriptions about the e-commerce services level in Indonesia.
arXiv Detail & Related papers (2021-02-18T01:33:14Z) - COOKIE: A Dataset for Conversational Recommendation over Knowledge
Graphs in E-commerce [64.95907840457471]
We present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.
The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation.
arXiv Detail & Related papers (2020-08-21T00:11:31Z) - Using Large Pretrained Language Models for Answering User Queries from
Product Specifications [0.0]
We propose an approach to automatically create a training dataset for this problem.
Our model gives a good performance even when trained on one vertical and tested across different verticals.
arXiv Detail & Related papers (2020-05-29T14:52:33Z)
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.