Intelligent Model Update Strategy for Sequential Recommendation

Zheqi Lv, Wenqiao Zhang, Zhengyu Chen, Shengyu Zhang, Kun Kuang

Keywords: Edge-Cloud Collaboration, Mis-Recommendation Detection, Out-of-Distribution Detection, Sequential Recommendation

Abstract: Recommendation systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications, which recently present two emerging trends: (i) Collaboration: single-sided model trained on-cloud (separate learning)→the device-cloud collaborative recommendation (collaborative learning). (ii) Real-time Dynamic: the network parameters are the same across all the instances (static model)→adaptive network parameters generation conditioned on the real-time instances (dynamic model). The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication. Despite promising, we argue that most of the communications are unnecessary to request the new parameters of the recommendation system on the cloud since the on-device data distribution are not always changing. To alleviate this issue, we designed a Intelligent Edge-Cloud Parameter Request Model (IntellectReq) that can be deployed on the device to calculate the request revenue with low resource consumption, so as to ensure the adaptive device-cloud communication with high revenue.We envision a new device intelligence learning task to implement IntellectReq by detecting the data out-of-domain. Moreover, we map the user’s real-time behavior to a normal distribution, the uncertainty is calculated by the multi-sampling outputs to measure the generalization ability of the device model to the current user behavior. Our experimental study demonstrates IntellectReq's effectiveness and generalizability on four public benchmarks, which yield a higher efficient device-cloud collaborative and dynamic recommendation paradigm.

Track: User Modeling and Recommendation

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Submission Number: 1