Since the creation of the Web, recommender systems (RSs) have been an
indispensable mechanism in information filtering. State-of-the-art RSs
primarily depend on categorical features, which ecoded by embedding vectors,
resulting in excessively large embedding tables. To prevent over-parameterized
embedding tables from harming scalability, both academia and industry have seen
increasing efforts in compressing RS embeddings. However, despite the
prosperity of lightweight embedding-based RSs (LERSs), a wide diversity is seen
in evaluation protocols, resulting in obstacles when relating LERS performance
to real-world usability. Moreover, despite the common goal of lightweight
embeddings, LERSs are evaluated with a single choice between the two main
recommendation tasks -- collaborative filtering and content-based
recommendation. This lack of discussions on cross-task transferability hinders
the development of unified, more scalable solutions. Motivated by these issues,
this study investigates various LERSs' performance, efficiency, and cross-task
transferability via a thorough benchmarking process. Additionally, we propose
an efficient embedding compression method using magnitude pruning, which is an
easy-to-deploy yet highly competitive baseline that outperforms various complex
LERSs. Our study reveals the distinct performance of LERSs across the two
tasks, shedding light on their effectiveness and generalizability. To support
edge-based recommendations, we tested all LERSs on a Raspberry Pi 4, where the
efficiency bottleneck is exposed. Finally, we conclude this paper with critical
summaries of LERS performance, model selection suggestions, and underexplored
challenges around LERSs for future research. To encourage future research, we
publish source codes and artifacts at \href{this
link}{this https URL}.

这项研究通过全面的基准测试过程，对轻量级嵌入式推荐系统（LERSs）的性能、效率和跨任务可转移性进行了调查，并提出了一种高效的嵌入式压缩方法，采用幅度剪枝，克服了现有复杂 LERSs 方法的竞争力，并揭示了 LERSs 在协同过滤和基于内容的推荐两个任务中的独特性能以及其有效性和普适性的启示。