Description

Search personalization and diversification are often seen as opposing alternatives to cope with query uncertainty, where, given an ambiguous query, it is either preferable to adapt the search result to a specific aspect that may interest of the user (personalization) or to regard multiple aspects in order to maximize the probability that some query aspect is relevant to the user (diversification). In this work, we refute this antagonistic view, and hypothesize that these two directions may in fact be effectively combined and enhance each other. Existing Web service recommendation approaches can be roughly divided into three categories: CF-based approaches, content-based approaches, and hybrid approaches. In the following, we survey the related work on service recommendation in these three categories, and on diversity-based ranking algorithms. There are drawbacks for these approaches. In this project the system proposed an innovative diversity-aware Web service ranking algorithm to rank the Web service candidates based on their scores, and diversity degrees derived from the Web service graph. The proposed approach will facilitate the research and development of web services, e-services, intelligent systems and business intelligence.