In this paper we provide a description of the methods we used as team BanaNeverAlone for the ACM RecSys Challenge 2020, organized by Twitter. The challenge addresses the problem of user engagement prediction: the goal is to predict the probability of a user engagement (Like, Reply, Retweet or Retweet with comment), based on a series of past interactions on the Twitter platform. Our proposed solution relies on several features that we extracted from the original dataset, as well as on consolidated models, such as gradient boosting for decision trees and neural networks. The ensemble model, built using blending, and a multi-objective optimization allowed our team to rank in position 4. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Classification and regression trees; Neural networks.

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Nowadays a wide range of applications is constrained by lowlatency requirements that cloud infrastructures cannot meet. Multiaccess Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reducing latency, but new challenges arise: edge nodes are resourceconstrained, the workload can vary significantly since users are nomadic, and task complexity is increasing (e.g., machine learning inference). To overcome these problems, the paper presents NEP-TUNE, a serverless-based framework for managing complex MEC solutions. NEPTUNE i) places functions on edge nodes according to user locations, ii) avoids the saturation of single nodes, iii) exploits GPUs when available, and iv) allocates resources (CPU cores) dynamically to meet foreseen execution times. A prototype, built on top of K3S, was used to evaluate NEPTUNE on a set of experiments that demonstrate a significant reduction in terms of response time, network overhead, and resource consumption compared to three well-known approaches. CCS CONCEPTS• Theory of computation → Scheduling algorithms; • Computing methodologies → Distributed computing methodologies; • Computer systems organization → Distributed architectures.