4.7 KiB
- Mdnotes File Name: sunConvergenceRecommenderSystems2020
Green Annotations (18/12/2020, 15:41:24)
"recommender systems have been used as an effective technology to lter useless information and attempt to recommend the most useful items" (Sun et al 2020:47118)
USE OF RECSYS (note on p.47118)
"Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service." (Sun et al 2020:47118)
"collaborative ltering (CF)" (Sun et al 2020:47118)
"content-based recommendation (CB)" (Sun et al 2020:47118)
"nowledge-based recommendation (KB)" (Sun et al 2020:47118)
"hybrid recommendation (HR)" (Sun et al 2020:47118)
"CF achieves the best accuracy of predictions about how much someone is going to enjoy a movie in Netix Prize, however, it has sparseness and cold-start problems [6]" (Sun et al 2020:47118)
"data sources contain the following key characteristics." (Sun et al 2020:47119)
"Sparsity: in the edge environment, the historical data sources stored in edge server comes from a small amount or even one user's proles" (Sun et al 2020:47119)
"Heterogeneity: edge devices are produced by companies around the world" (Sun et al 2020:47119)
"Mobility: the mobility is an inherent characteristic of users in the mobile networks" (Sun et al 2020:47119)
"Volatility: the state of the mobile edge network is volatility, when one user invokes a service many times, the QoS data may be different each time." (Sun et al 2020:47119)
"recommender systems based on cloud computing have been proposed in traditional Internet environments, they are gradually unable to deal with these novel emerging services and massively distributed data in mobile edge network, they may fail to predict what users' interests and demands are." (Sun et al 2020:47119)
THIS IS A MOTIVATION STATEMENT!
CHALLENGE (note on p.47119)
"1) cold-start problem, as data sources of active users are usually very sparse, even new or inactive users lack relevant proles, the cold-start problem occurs" (Sun et al 2020:47119)
"2) exploration and exploitation problem, for example, in online shopping, exploration implies recommending new goods and exploitation entails reusing existing goods. How to nd an optimal trade-off between exploration and exploitation is crucial issu" (Sun et al 2020:47119)
") security and privacy problem, the data sources are produced by various IoT devices and distributed at different edge platforms, resulting in potential leakage of user data security problem" (Sun et al 2020:47119)
"Mobile edge computing (MEC)" (Sun et al 2020:47119)
"four enabling technologies for building recommender systems and Edge computing:" (Sun et al 2020:47120)
") Recommender systems on Edge" (Sun et al 2020:47120)
"2) Recommender systems in Edge" (Sun et al 2020:47120)
"3) Edge computing for recommender systems" (Sun et al 2020:47120)
"4) Recommender systems for Edge computing" (Sun et al 2020:47120)
"However, conventional recommender systems are gradually unable to meet the requirements of IoT services. Recently, a novel computing paradigm has been proposed by pushing computation and storage resources from the central cloud servers to network edges. Hence, deploying recommender systems applications at the edge servers can perform some lightweight processing to improve QoS." (Sun et al 2020:47125)