Recommender systems have been successfully used to address the problem of information
overload, where consumers of goods and services have too many choices and …

Identifying correlations between context data, user behavior, and semantic information can
lead to new services that are able to adapt to different situations. This" personalization" …

Data sparsity and cold-start remains to be the main limitations and weaknesses in
recommendation systems that employ collaborative filtering (CF). These limitations cause …

For large software projects, system designers have to adhere to a significant number of
functional and non-functional requirements, which makes software development a complex …

Advances in telecommunications and information technology have allowed the proliferation
of mobile and multifunctional devices and their incorporation more and more into physical …

Given the large amount of existing services and the diversified needs nowadays, it is time-
consuming for end-users to find appropriate services. To help end-users obtain their desired …

Existing service recommendation methods, that employ memory-based collaborative filtering
(CF) techniques, compute the similarity between users or items using nonfunctional attribute …

In this paper we study the synergy between user behavior, context data, and semantic
information in order to enable future services to adapt to different situations based on the …

As the abundance of web services on the World Wide Web increase, designing effective
approaches for web service selection and recommendation has become more and more …

The goal of the work in this paper is towards the incorporation of context in recommender
systems in the domain of mobile applications. The approach recommends mobile …