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Diversified Quality Centric Service Recommendation
2019 IEEE International Conference on Web Services (ICWS), 2019In the past decade, a large number and variety of services have been published on the Internet, e.g., public services, web services, cloud services, etc., offering customers a wide range of choices for fulfilling their demands. This has made it a challenge for customers to select from many mutually-substitutable competing services, especially when they
Yiwen Zhang +5 more
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Taxonomy for Personalized Recommendation Service
2008 International Symposium on Electronic Commerce and Security, 2008The amount of information in the world is increasing far more quickly than our ability to process it. Recommender systems are used by e-commerce sites to suggest products to their customers and to provide consumers with information to help them determine which products to purchase.
Li Yu, Ming Dong, Rong Wang
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Clustering-Based Diversity in Service Recommendation
Recommending a certain number of services based on QoS (Quality of Service) is a very widespread problem. Traditional approaches use the QoS advertised by service providers to calculate the top-k services or the skyline ones. The drawback of the top-k operator is that it requires users to provide a utility function, which is a rather demanding task. InBenouaret, Karim +1 more
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Visual Service Diagnostics and Service Provider Recommendation
Abstract - Home services management suffers from inefficiencies created by manual issue reporting, miscommunication, and delays in the allocation of service providers. Traditionally, the methods are reliant on users saying what the problems are, leading to wrong service assignments and increased time to resolve the problems.P Haneefa, Sabhamol, Mathew, Meera Rose
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On Bootstrapping Web Service Recommendation
2013We present a novel framework to bootstrap Web Service recommendation. Service recommendation has become an effective means to achieve personalized service selection. It leverages past user-service interaction information to accurately predict user preference on previously unknown services.
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Smart Recommendation of Telecommunication Services
2021 29th National Conference with International Participation (TELECOM), 2021Ivan Ganchev +2 more
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Location-Aware Deep Collaborative Filtering for Service Recommendation
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021Yin Chunhui, Qilin Wu, Qiang He
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