Details

Autor(en) / Beteiligte

• Kang, Guosheng
• Liang, Bowen
• Ding, Linghang
• Liu, Jianxun
• Cao, Buqing
• Kang, Yun

Titel

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

Ist Teil von

• Engineering reports (Hoboken, N.J.), 2024-01, Vol.6 (1), p.n/a

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2024

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results. This article proposes to mine a user's diversity preference from the user's service invocation history, and generate a personalized Web service recommendation list with preferred diversity based on the diversity adjustment algorithm.