Graph-Based Recommendation System

07/31/2018
by   Kaige Yang, et al.
0

In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/02/2016

Graph Clustering Bandits for Recommendation

We investigate an efficient context-dependent clustering technique for r...
research
09/21/2020

Bandits Under The Influence (Extended Version)

Recommender systems should adapt to user interests as the latter evolve....
research
12/02/2021

Recommending with Recommendations

Recommendation systems are a key modern application of machine learning,...
research
06/18/2020

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect

We study the effect of persistence of engagement on learning in a stocha...
research
07/12/2019

Laplacian-regularized graph bandits: Algorithms and theoretical analysis

We study contextual multi-armed bandit problems in the case of multiple ...
research
10/15/2018

Regret vs. Bandwidth Trade-off for Recommendation Systems

We consider recommendation systems that need to operate under wireless b...
research
05/12/2022

Improving Sequential Query Recommendation with Immediate User Feedback

We propose an algorithm for next query recommendation in interactive dat...

Please sign up or login with your details

Forgot password? Click here to reset