CDRec-CAS: Cross-Domain Recommendation Using Context-Aware Sequences

Anwar, Taushif; Uma, V.; Srivastava, Gautam

doi:10.1109/tcss.2022.3233781

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…However, this study aimed to develop large synthetic datasets for a specific domain, such as the MovieLens 20M. In addition, with the new solution for the RS, there was some research such as CD-SPM, and CDRec-CAS ( Anwar & Uma, 2019 ; Anwar, Uma & Srivastava, 2023 ). These studies focus to solve problems in cross-domain RS and this lead to the need for synthetic cross-domain datasets to verify the performance of the proposed methods more effectively.…”

Section: Related Workmentioning

confidence: 99%

DaGzang: a synthetic data generator for cross-domain recommendation services

Nguyen

Jung

2023

PeerJ Computer Science

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Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon’s e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).

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Section: Related Workmentioning

confidence: 99%