reoneo97 / wutr-buildon-2021

Team WUTR Repo for BuildOn 2021

wutr-buildon-2021

Team WUTR Repo for BuildOn 2021

Backend: FastAPI Frontend: Vue.js

Test: Scripts to populate dataset

Database Schemas

Database is using DynamoDB which is a No-SQL Database and relies on storing key-value pairs. There are 4 main tables

Listing Table

Contains all the listing information

class Listing(BaseModel):
    name: str
    description: str
    price: Decimal
    for_sale: bool
    images: List[ListingImage] : # File path from S3
    user: str
    tags: List[str] = list()
    created_timestamp: str
    id:Optional[str] = None

User Database

Contains User information

Views Database

For each user contains the listings that the user has interacted with

• This is used for the recommendation system

User_Feed Database

• Contains a List of ListingIds for each user.
• The API will only pull items from this database and then remove them if the user has already clicked on the listing
• Adding additional Ids to this database will be done by AWS Lambda Functions

Machine Learning

Price Prediction Model

SentenceBert which uses Siamese neural networks to evaluate whether items are similar or not

• For our case we chose to use a smaller version of BERT DistilBert because of our smaller training size.
  • Faster inference and because our price dataset is smaller
  • Allows room for scaling up the model when we get more data

@article{DBLP:journals/corr/abs-1908-10084, author = {Nils Reimers and Iryna Gurevych}, title = {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, journal = {CoRR}, volume = {abs/1908.10084}, year = {2019}, url = {http://arxiv.org/abs/1908.10084}, archivePrefix = {arXiv}, eprint = {1908.10084}, timestamp = {Thu, 26 Nov 2020 12:13:54 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-10084.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }