On hybrid modular recommendation systems for video streaming

The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the tester for assessing the performance of the system. The enabler incorporates a variety of recommendation algorithms that span from collaborative filtering and content-based techniques to ones based on neural networks. It uses the nested cross validation for automatically selecting the best ML algorithm along with its hyper-parameter values for the given input, according to a specific metric. The enabler can be easily extended to include other recommenders and blenders. The enabler has been extensively evaluated in the context of video-streaming. It outperforms various other algorithms, when tested on the Movielens 1M benchmark dataset.encouraging results. Moreover For example, it achieves an RMSE of 0.8206, compared to the state-of-the-art performance of the AutoRec and SVD, 0.827 and 0.845, respectively. A pilot web-based recommendation system was developed and tested in the production environment of a large telecom operator in Greece. Volunteer customers of the video-streaming service provided by the telecom operator employed the system in the context of an out-in-the-wild field study with a post-analysis of the enabler, using the collected ratings of the pilot, demonstrated that it significantly outperforms several popular recommendation algorithms

Performance of information discovery and message relaying in mobile ad hoc networks

This paper presents 7DS, a novel peer-to-peer resource sharing system. 7DS is an architecture, a set of protocols and an implementation enabling the exchange of data among peers that are not necessarily connected to the Internet. Peers can be either mobile or stationary. We focus on three different facets of cooperation, namely, data sharing, message relaying and network connection sharing. 7DS enables wireless devices to discover, disseminate, relay information among each other to increase the data access. We evaluate via extensive simulations the effectiveness of our system for data dissemination and message relaying among mobile devices with a large number of user mobility scenarios. We model several general data dissemination approaches and investigate the effect of the wireless coverage range, 7DS host density, and cooperation strategy among the mobile hosts as a function of time. We also present a power conservation mechanism that is beneficial, since it increases the power savings, without degrading the data dissemination. Using theory from random walks, random environments and diffusion of controlled processes, we model one of these data dissemination schemes and show that the analysis confirms the simulation results for this scheme

Towards a Causal Analysis of Video QoE from Network and Application QoS

International audienceThe relationship between the user perceived Quality of Experience (QoE) with Internet applications and the Quality of Service (QoS) of the underlying network and applications is complex. Unveiling statistical relations between QoE and QoS can boost the prediction and diagnosis of QoE. In this paper, we shed light on the relationship between QoE and QoS for a popular application: YouTube video streaming. We conducted a controlled study where we asked users to rate their perceived quality of YouTube videos under different network conditions. During this experiments, we also captured network QoS and application QoS. We then analyze the resulting dataset with SES, a feature selection algorithm that identifies minimal-size, statistically-equivalent signatures with maximal predictive power for a target variable (e.g., QoE). We found that we can build optimal QoE predictors using a minimal signature of only three features from application or network QoS metrics compared to four when we consider features from both layers