FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308148980> ?p ?o ?g. }

• W4308148980 endingPage "110081" @default.
• W4308148980 startingPage "110081" @default.
• W4308148980 abstract "In the real-world cloud computing scenarios, a user usually invokes few Web services and can provide only a small proportion of QoS (Quality of Service) records. Therefore, for a whole range of services, many records for this user are missing (i.e. incomplete QoS information). These missing QoS values make it difficult to conduct accurate service recommendations. To overcome this difficulty, it is necessary to predict the missing QoS values for the user. Most existing algorithms tackle the QoS prediction problem through aggregating the QoS values of the local similar neighbors of the user, which easily leads to local optimization. In this paper, we model it as a global search optimization problem in the distribution space of QoS values and propose a novel algorithm PSO-USRec. In the algorithm, particle swarm optimization (PSO) is customized and improved by diversifying the initial solutions and smoothing the outlier particles. To validate the effectiveness of the PSO-USRec algorithm, comparison experiments are conducted on a well-known public QoS dataset. The experimental results show that the PSO-USRec algorithm significantly outperforms the state-of-the-art collaborative filtering approaches. It reduces MAE and RMSE by at least 5.42% and 1%, and at most 14.29% and 2.25%, respectively." @default.
• W4308148980 created "2022-11-08" @default.
• W4308148980 creator A5005231122 @default.
• W4308148980 creator A5052028589 @default.
• W4308148980 creator A5091535709 @default.
• W4308148980 date "2023-01-01" @default.
• W4308148980 modified "2023-10-17" @default.
• W4308148980 title "QoS prediction for web services in cloud environments based on swarm intelligence search" @default.
• W4308148980 cites W1529703297 @default.
• W4308148980 cites W1965310012 @default.
• W4308148980 cites W2003274231 @default.
• W4308148980 cites W2016210396 @default.
• W4308148980 cites W2024373430 @default.
• W4308148980 cites W2026749761 @default.
• W4308148980 cites W2038540196 @default.
• W4308148980 cites W2042281163 @default.
• W4308148980 cites W2045344089 @default.
• W4308148980 cites W2079363226 @default.
• W4308148980 cites W2088950591 @default.
• W4308148980 cites W2091381870 @default.
• W4308148980 cites W2092847604 @default.
• W4308148980 cites W2093063389 @default.
• W4308148980 cites W2104185402 @default.
• W4308148980 cites W2109966714 @default.
• W4308148980 cites W2131077880 @default.
• W4308148980 cites W2140597141 @default.
• W4308148980 cites W2142144955 @default.
• W4308148980 cites W2146356176 @default.
• W4308148980 cites W2152208379 @default.
• W4308148980 cites W2155106456 @default.
• W4308148980 cites W2159094788 @default.
• W4308148980 cites W2219225017 @default.
• W4308148980 cites W2295458067 @default.
• W4308148980 cites W2317591930 @default.
• W4308148980 cites W2323283582 @default.
• W4308148980 cites W2522691827 @default.
• W4308148980 cites W2524052287 @default.
• W4308148980 cites W2537653515 @default.
• W4308148980 cites W2539391377 @default.
• W4308148980 cites W2573212983 @default.
• W4308148980 cites W2606719072 @default.
• W4308148980 cites W2783666173 @default.
• W4308148980 cites W2795153700 @default.
• W4308148980 cites W2888239087 @default.
• W4308148980 cites W2902493494 @default.
• W4308148980 cites W2968548800 @default.
• W4308148980 cites W2973261831 @default.
• W4308148980 cites W2978648800 @default.
• W4308148980 cites W2991658140 @default.
• W4308148980 cites W3004823354 @default.
• W4308148980 cites W3008049802 @default.
• W4308148980 cites W3011380968 @default.
• W4308148980 cites W3015366120 @default.
• W4308148980 cites W3025050471 @default.
• W4308148980 cites W3028305817 @default.
• W4308148980 cites W3101685505 @default.
• W4308148980 cites W3156564332 @default.
• W4308148980 cites W3202426913 @default.
• W4308148980 cites W3208323717 @default.
• W4308148980 cites W3214703494 @default.
• W4308148980 cites W4200161340 @default.
• W4308148980 cites W4224300633 @default.
• W4308148980 doi "https://doi.org/10.1016/j.knosys.2022.110081" @default.
• W4308148980 hasPublicationYear "2023" @default.
• W4308148980 type Work @default.
• W4308148980 citedByCount "2" @default.
• W4308148980 countsByYear W43081489802023 @default.
• W4308148980 crossrefType "journal-article" @default.
• W4308148980 hasAuthorship W4308148980A5005231122 @default.
• W4308148980 hasAuthorship W4308148980A5052028589 @default.
• W4308148980 hasAuthorship W4308148980A5091535709 @default.
• W4308148980 hasConcept C111919701 @default.
• W4308148980 hasConcept C119857082 @default.
• W4308148980 hasConcept C124101348 @default.
• W4308148980 hasConcept C136764020 @default.
• W4308148980 hasConcept C154945302 @default.
• W4308148980 hasConcept C31258907 @default.
• W4308148980 hasConcept C31972630 @default.
• W4308148980 hasConcept C35578498 @default.
• W4308148980 hasConcept C3770464 @default.
• W4308148980 hasConcept C41008148 @default.
• W4308148980 hasConcept C5119721 @default.
• W4308148980 hasConcept C79337645 @default.
• W4308148980 hasConcept C79974875 @default.
• W4308148980 hasConcept C85617194 @default.
• W4308148980 hasConcept C9357733 @default.
• W4308148980 hasConceptScore W4308148980C111919701 @default.
• W4308148980 hasConceptScore W4308148980C119857082 @default.
• W4308148980 hasConceptScore W4308148980C124101348 @default.
• W4308148980 hasConceptScore W4308148980C136764020 @default.
• W4308148980 hasConceptScore W4308148980C154945302 @default.
• W4308148980 hasConceptScore W4308148980C31258907 @default.
• W4308148980 hasConceptScore W4308148980C31972630 @default.
• W4308148980 hasConceptScore W4308148980C35578498 @default.
• W4308148980 hasConceptScore W4308148980C3770464 @default.
• W4308148980 hasConceptScore W4308148980C41008148 @default.
• W4308148980 hasConceptScore W4308148980C5119721 @default.
• W4308148980 hasConceptScore W4308148980C79337645 @default.

• next