Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data

Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in order to predict the missing ratings in the sparse domain (target). In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique. Thereafter we apply the Maximum Margin Matrix factorization (MMMF) technique on the codebook in order to learn the user and item latent features of codebook. Prediction of the target rating matrix is achieved by introducing these latent features in a novel way into the optimisation function. In the experiments we demonstrate that our model improves the prediction accuracy of the target matrix on benchmark datasets.

READ FULL TEXT

page 7

page 8

research
08/02/2021

A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data

Recommender systems(RS), especially collaborative filtering(CF) based RS...
research
03/05/2018

Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping

Collaborative Filtering (CF) is a widely adopted technique in recommende...
research
05/21/2020

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has agg...
research
10/26/2012

Selective Transfer Learning for Cross Domain Recommendation

Collaborative filtering (CF) aims to predict users' ratings on items acc...
research
02/10/2022

Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation

Cross-Domain Recommendation (CDR) has been popularly studied to utilize ...
research
06/24/2023

Cross-domain Recommender Systems via Multimodal Domain Adaptation

Collaborative Filtering (CF) has emerged as one of the most prominent im...
research
09/26/2018

A novel approach for venue recommendation using cross-domain techniques

Finding the next venue to be visited by a user in a specific city is an ...

Please sign up or login with your details

Forgot password? Click here to reset