Abstract
The last decade has witnessed a tremendous
growth of Web services as a major
technology for sharing data, computing
resources, and programs on the Web. With
increasing adoption and presence of Web
services, designing novel approaches for
efficient and effective Web service
recommendation has become of paramount
importance. In existing web services
discovery and recommendation approaches
focus on keyword-dominant Web service
search engines, which possess many
limitations such as poor recommendation
performance and heavy dependence on
correct and complex queries from users.
Recent research efforts on Web service
recommendation center on two prominent
approaches: collaborative filtering and
content-based recommendation. Unfortunately, both approaches have some
drawbacks, which restrict their applicability
in Web service recommendation. In
proposed system for recommendation we
will be using Agglomerative Hierarchal
Clustering or Hierarchal Agglomerative
Clustering for effective recommendation in
web-services. our approach considers
simultaneously both rating data (e.g., QoS)
and semantic content data (e.g.,
functionalities) of Web services using a
probabilistic generative model.
Index Terms—Collaborative filtering,
incremental model, matrix factorization,
recommender system, combined scheme,
static model.
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