Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations

11/29/2020
by   Anubha Kabra, et al.
0

Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The Duelling Bandit based exploration provides robust exploration as compared to the state-of-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as com-pared to existing techniques.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/04/2018

Exploration by Distributional Reinforcement Learning

We propose a framework based on distributional reinforcement learning an...
research
12/09/2020

Interactive Search Based on Deep Reinforcement Learning

With the continuous development of machine learning technology, major e-...
research
10/15/2020

Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning

We tackle tag-based query refinement as a mobile-friendly alternative to...
research
02/11/2019

Model-Based Reinforcement Learning for Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have be...
research
08/20/2022

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

The recent popularity of edge devices and Artificial Intelligent of Thin...
research
01/19/2022

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams

In this paper, we focus on the problem of modeling dynamic geo-human int...

Please sign up or login with your details

Forgot password? Click here to reset