A Recommender System Based on a Double Feature Allocation Model

02/02/2022
by   Qiaohui Lin, et al.
0

A collaborative filtering recommender system predicts user preferences by discovering common features among users and items. We implement such inference using a Bayesian double feature allocation model, that is, a model for random pairs of subsets. We use an Indian buffet process (IBP) to link users and items to features. Here a feature is a subset of users and a matching subset of items. By training feature-specific rating effects, we predict ratings. We use MovieLens Data to demonstrate posterior inference in the model and prediction of user preferences for unseen items compared to items they have previously rated. Part of the implementation is a novel semi-consensus Monte Carlo method to accomodate large numbers of users and items, as is typical for related applications. The proposed approach implements parallel posterior sampling in multiple shards of users while sharing item-related global parameters across shards.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/22/2019

Learning from Sets of Items in Recommender Systems

Most of the existing recommender systems use the ratings provided by use...
research
10/20/2018

Temporal Proximity induces Attributes Similarity

Users consume their favorite content in temporal proximity of consumptio...
research
08/11/2023

Topic-Level Bayesian Surprise and Serendipity for Recommender Systems

A recommender system that optimizes its recommendations solely to fit a ...
research
06/27/2012

Visualization of Collaborative Data

Collaborative data consist of ratings relating two distinct sets of obje...
research
12/04/2019

Scalable Bayesian Preference Learning for Crowds

We propose a scalable Bayesian preference learning method for jointly pr...
research
04/30/2021

Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling

We consider the problem of recommending relevant content to users of an ...
research
11/20/2019

Gradient-based Optimization for Bayesian Preference Elicitation

Effective techniques for eliciting user preferences have taken on added ...

Please sign up or login with your details

Forgot password? Click here to reset