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Current thinking about the significance of the WHO and its recommendations approaches deliberate mystification.
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In the collaborative recommendation approaches [17, 20, 30], the system recommends alternatives queries to the current user using his query and the query log containing previous user's queries.
In the individual recommendation approaches [25, 26, 40], the system provides alternatives and anticipated recommended query using a user profile.
The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended.
Aissi et al. [2] distinguish two main research orientations in the domain of OLAP recommendation: (1) Collaborative recommendation approaches and (2) Individual recommendation approaches.
The pure and mixed recommendation approaches lead to starkly different recommendation networks (see Fig. 9).
This paper is organized as follows: "Recommendation approach: state of the art" presents a state of the art on recommendation approaches in DWs.
Our approach is different from general matrix completion-based recommendation approaches on two major aspects.
Section 3 describes BROAD-RSI infrastructure, technologies solution, requirements, data workflow and recommendation approaches.
Besides, different data sets and users may put varied weight on these two recommendation approaches.
That's why researchers went for recommendation approaches trying to find a solution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com