A significant response to the information overload problem currently being experienced as a result of the enormous advent of internet usage has been demonstrated by recommendation systems; that is, providing users with adapted information services. User preferences play a key role in preparing recommendations in the search for required information over the web. User feedback, both explicit and implicit, has proven to be vital for recommendation systems, with the similarity between users then able to be computed. In this paper we propose that traditional reliance on user similarity may be overstated. Nevertheless, there are many problems to be faced, specifically; sparseness, cold start, prediction accuracy, as well as scalability, which can all, result in a challenge of trust over the recommendation systems. A sparsity rate of 95% has been experienced in CF-based commercial recommendation applications. We discuss the manner in which other factors have a vital role in managing recommendations. Specifically, we propose that the issue of user satisfaction must be considered and incorporated with explicit feedback for improved recommendations.
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