A service which creates a user preferences profile in order to solve the cold start issue for the recommending systems
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Updated
Feb 15, 2017 - Python
A service which creates a user preferences profile in order to solve the cold start issue for the recommending systems
A recommender engine built for a Bay Area online dating website to maximize the successful matches by introducing hybrid recommender system and reverse match technique.
Our solution for Recsys Challenge 2017.
Example lightweight DynamoDB integration that limits AWS Lambda cold starts for Java
[ACL2018] Cold-Start Aware User and Product Attention for Sentiment Classification
Collaborating Filtering Project
Case study project for recommending movies to users.
Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.
This repository includes some papers that I have read or which I think may be very interesting.
AWS low-level API client
AWS Lambda custom runtime with GraalVM native-image binary
Java library for building native AWS Lambda runtimes
Personalised and popularity-based movie recommendations.
Prebaking technique paper artifacts for the ACM/IFIP Middleware 2020 conference.
Data generation, model training and evaluation pipelines for the cold-start setting.
Deep-learning-based service recommendation in a cold-start scenario of mashup development
Notes I made on Azure Functions cold-start behavior
[WWW 2021]Task-adaptive Neural Process for User Cold-Start Recommendation
Social network link prediction using graph mining and machine learning
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