LACFAC-location-aware collaborative filtering and association-based clustering approach for web service recommendation
M. Jenifer, S. Thabasu Kannan · International Journal of Web Engineering and Technology · 2018
The spread over of huge amount of information in the internet makes it really difficult for the users to obtain the relevant search items. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfil the need of internet user, there is a need to know their preferences of search at various contexts. Hence, it is preferred to select the web service with best quality of service (QoS) performance to satisfy the needs of user. This paper presents a location-aware collaborative filtering (CF) and association-based clustering approach for web service recommendation. The similarity between users and web services is measured by considering the personalised deviation of QoS of web services and QoS experiences of users. Hence, web service recommendation becomes a really challenging and time-consuming task due to the large search space. To reduce the search space, clustering of the web services into clusters is an efficient approach. The services are clustered based on the semantic similarity and association between them. Our proposed approach recommends services using the generated clusters and services with better QoS values.