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Inf. Syst."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            Since the creation of the Web, recommender systems (RSs) have been an indispensable personalization mechanism in information filtering. Most state-of-the-art RSs primarily depend on categorical features such as user and item IDs, and use embedding vectors to encode their information for accurate recommendations, resulting in an excessively large embedding table owing to the immense feature corpus. To prevent the heavily parameterized embedding table from harming RSs\u2019 scalability, both academia and industry have seen increasing efforts compressing RS embeddings, and this trend is further amplified by the recent uptake in edge computing for online services. However, despite the prosperity of existing lightweight embedding-based RSs (LERSs), a strong diversity is seen in the evaluation protocols adopted across publications, resulting in obstacles when relating the reported performance of those LERSs to their real-world usability. On the other hand, among the two fundamental recommendation tasks, namely traditional collaborative filtering and content-based recommendation, despite their common goal of achieving lightweight embeddings, the outgoing LERSs are designed and evaluated with a straightforward \u201ceither-or\u201d choice between the two tasks. Consequently, the lack of discussions on a method\u2019s cross-task transferability will likely hinder the development of unified, more scalable solutions for production environments. Motivated by these unresolved issues, this study aims to systematically investigate existing LERSs\u2019 performance, efficiency, and cross-task transferability\n            <jats:italic>via<\/jats:italic>\n            a thorough benchmarking process. To create a generic, task-independent baseline, we propose an efficient embedding compression approach based on magnitude pruning, which is proven to be an easy-to-deploy yet highly competitive baseline that outperforms various complex LERSs. Our study reveals the distinct performance of different LERSs across the two recommendation tasks, shedding light on their effectiveness and generalizability under different settings. Furthermore, to account for edge-based recommendation\u2014an increasingly popular use case of LERSs, we have also deployed and tested all LERSs on a Raspberry Pi 4, where their efficiency bottleneck is exposed compared with GPU-based deployment. Finally, we conclude this article with critical summaries on the performance comparison, suggestions on model selection based on task objectives, and underexplored challenges around the applicability of existing LERSs for future research. To encourage and support future LERS research, we publish all source codes and data, checkpoints, and documentation at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/chenxing1999\/recsys-benchmark\">https:\/\/github.com\/chenxing1999\/recsys-benchmark<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3712589","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T14:31:08Z","timestamp":1737124268000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7615-3167","authenticated-orcid":false,"given":"Hung Vinh","family":"Tran","sequence":"first","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-146X","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9687-1315","authenticated-orcid":false,"given":"Nguyen","family":"Quoc Viet Hung","sequence":"additional","affiliation":[{"name":"Griffith University, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-4949","authenticated-orcid":false,"given":"Zi","family":"Huang","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-8883","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-261X","authenticated-orcid":false,"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2014. 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