Data Context Adaptation for Accurate Recommendation with Additional Information
Hyunsik Jeon, Bonhun Koo, U Kang

TL;DR
This paper introduces DaConA, a neural network-based recommendation method that adapts to different data contexts, models independence explicitly, and learns non-linear interactions, achieving state-of-the-art accuracy.
Contribution
DaConA is a novel neural network approach that addresses data context differences, models independence explicitly, and learns non-linear interactions for improved recommendations.
Findings
DaConA outperforms existing methods in accuracy on real-world datasets.
It generalizes standard matrix factorization and collective matrix factorization.
The data context adaptation layer effectively captures relevant features.
Abstract
Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods are limited in that 1) they ignore the fact that data contexts of rating and auxiliary matrices are different, 2) they have restricted capability of expressing independence information of users or items, and 3) they assume the relation between a user and an item is linear. We propose DaConA, a neural network based method for recommendation with a rating matrix and an auxiliary matrix. DaConA is designed with the following three main ideas. First, we propose a data context adaptation layer to extract pertinent features for different data contexts. Second, DaConA represents…
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