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Authors: Tahar-Rafik Boudiba and Taoufiq Dkaki

Affiliation: IRIT, UMR 5505 CNRS, 118 Route de Narbonne, F-31062 Toulouse Cedex 9, France

Keyword(s): Folksonomies, Deep Learning, Tag-based Embedding, Social Tagging, Recommendation

Abstract: Neural collaborative filtering approaches are mainly based on learning user-item interactions. Since in collaborative systems, there are several contents surrounding users and items, essentially user reviews or user tags these personal contents are valuable information that can be leveraged with collaborative filtering approaches. In this context, we address the problem of integrating such content into a neural collaborative filtering model for rating prediction. Such content often represented using the bag of words paradigm is subject to ambiguity. Recent approaches suggest the use of deep neuronal architectures as they attempt to learn semantic and contextual word representations. In this paper, we extended several neural collaborative filtering models for rating prediction that were initially intended to learn user-item interaction by adding textual content. We describe an empirical study that evaluates the impact of using static or contextualized word embeddings with a neural col laborative filtering strategy. The presented models use dense tag-based user and item representations extracted from pre-trained static Word2vec and contextual BERT. The Models were adapted using MLP and Autoencoder architecture and evaluated on several MovieLens datasets. The results showed good improvements when integrating contextual tag embeddings into such neural collaborative filtering architectures. (More)

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Paper citation in several formats:
Boudiba, T. and Dkaki, T. (2022). Exploring Contextualized Tag-based Embeddings for Neural Collaborative Filtering. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 158-166. DOI: 10.5220/0010793300003116

@conference{icaart22,
author={Tahar{-}Rafik Boudiba. and Taoufiq Dkaki.},
title={Exploring Contextualized Tag-based Embeddings for Neural Collaborative Filtering},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={158-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010793300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Exploring Contextualized Tag-based Embeddings for Neural Collaborative Filtering
SN - 978-989-758-547-0
IS - 2184-433X
AU - Boudiba, T.
AU - Dkaki, T.
PY - 2022
SP - 158
EP - 166
DO - 10.5220/0010793300003116
PB - SciTePress

- Science and Technology Publications, Lda.
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