research-article

A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem

Authors:

Vineet K. Sejwal,

Muhammad AbulaishAuthors Info & Claims

Volume 209, Issue C

https://doi.org/10.1016/j.eswa.2022.118307

Published: 15 December 2022 Publication History

Abstract

Recommender systems aim to estimate item ratings and recommend items based on the users’ interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various limitations, such as data sparsity, black-box recommendation, and cold-start problems. As a result, researchers have proposed amalgamating contextual information with rating data to provide effective recommendations. Although user-generated data in the form of reviews are a rich source of contextual information, they are rarely utilized in recommender algorithms. This study presents a hybrid recommendation technique, called RecTE, using rating data and topic embedding, which is an amalgamation of word embedding and topic modeling techniques. The novelty of RecTE lies in predicting item ratings using topic embeddings learned by incorporating local and global contextual information and integrating them with user-based collaborative filtering. RecTE is empirically evaluated over three real-world datasets – YelpNYC, YelpZip and TripAdvisor. This technique performs significantly better in comparison to nine baselines and five state-of-the-art recommendation techniques. On empirical analysis, we found that incorporating topic embedding in RecTE makes it capable of performing significantly better and handle cold-start problems effectively in comparison to the existing recommendation approaches.

Highlights

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A novel recommendation approach to handle the cold-start problem.

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Incorporating rating data and topic embedding into UBCF for rating prediction.

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Assessing the impact of topic embedding on effective recommendation.

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Comparative evaluation of the proposed approach with 9 baselines and 5 SOTA.

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Assessing efficacy of the proposed approach to handle the cold-start problem.

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Cited By

Zheng JChen SCao FPeng FHuang M(2024)Explainable recommendation based on fusion representation of multi-type feature embeddingThe Journal of Supercomputing10.1007/s11227-023-05831-x80:8(10370-10393)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05831-x
Saxena UAlam T(2023)Recommendation-based trust computation and rating prediction model for security enhancement in cloud computing systemsService Oriented Computing and Applications10.1007/s11761-023-00377-517:4(239-257)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11761-023-00377-5

Index Terms

A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 209, Issue C

Dec 2022

1590 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 15 December 2022

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Cited By

Zheng JChen SCao FPeng FHuang M(2024)Explainable recommendation based on fusion representation of multi-type feature embeddingThe Journal of Supercomputing10.1007/s11227-023-05831-x80:8(10370-10393)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11227-023-05831-x
Saxena UAlam T(2023)Recommendation-based trust computation and rating prediction model for security enhancement in cloud computing systemsService Oriented Computing and Applications10.1007/s11761-023-00377-517:4(239-257)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11761-023-00377-5

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