ariaattar/CASM-PyTorch
PyTorch Implementation of Context-Aware Sequential Model for Multi-Behaviour Recommendation https://arxiv.org/abs/2312.09684
This project helps e-commerce managers and product strategists improve product recommendations for customers. It takes a customer's history of interactions (like viewing, adding to cart, and purchasing) and suggests the next most relevant item. The output is a highly accurate list of personalized product recommendations, leading to increased customer engagement and sales.
No commits in the last 6 months.
Use this if you need to build a recommendation system that accurately predicts user preferences by considering multiple types of past interactions, not just purchases.
Not ideal if you only have a single type of user interaction data or are looking for a simple, non-sequential recommendation approach.
Stars
10
Forks
3
Language
Python
License
MIT
Category
Last pushed
May 31, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ariaattar/CASM-PyTorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
meta-pytorch/torchrec
Pytorch domain library for recommendation systems
recommenders-team/recommenders
Best Practices on Recommendation Systems
hongleizhang/RSPapers
RSTutorials: A Curated List of Must-read Papers on Recommender System.
kakao/buffalo
TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
RUCAIBox/CRSLab
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).