Context (archaeology)

World Wide Web

Linguistics

Philosophy

Biology

Distillation

Chromatography

Context awareness

Computer security

Operating system

Computer science

Paleontology

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Enhanced Data Rates for GSM Evolution

Inference

Artificial intelligence

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Chemistry

Recommender system

Wireless

Throughput

Phone

Session (web analytics)

Session-based recommender systems (SBR) are becoming increasingly popular because they can predict user interests without relying on long-term user profile and support login-free recommendation. Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental context to the server, raising the public concern about privacy. On-device recommender systems circumvent these two issues with cost-conscious settings and local inference. However, due to the limited memory and computing resources, on-device recommender systems are confronted with two fundamental challenges: (1) how to reduce the size of regular models to fit edge devices? (2) how to retain the original capacity?

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation