Sentence examples for textual recommendations from inspiring English sources

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The app personalizes the guidance based on the correlated measured data and delivers clear, textual recommendations with actionable content.

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The article is structured as follows: in "Related work" section we overview some works related to the use of users' textual reviews in the recommendation process; in "The proposed system" section we describe the proposed system.

As future work, we intend to: (1) explore multi-criteria recommendation using both textual reviews and multi-criteria ratings and (2) explore the users' reputation factor to model incoming ratings.

To develop appropriate recommendations of information items (textual and graphical), a visual interface must consider how graphical information-seeking behavior differs between children and adults.

Therefore, we enriched the CPG recommendation model with attributes for modelling the textual criticisms.

For example, when the user defines the type of the application, the Architectural Patterns tab is expanded containing the selected type related Architectural Patterns, and when a shape is dragged in the canvas, the Recommendations of Design Patterns tab expands, containing the recommended patterns that are produced based on the user's textual input.

The knowledge base is composed of (a) specific recommendations that directly match the CPG recommendations, enriched with textual labels for generating the critiques, and (b) generic recommendations that model the implicit, CPG-independent, medical knowledge required for the critiquing process.

Whereas, recent advances do not transcend limits of recommendation algorithms that solely use geographical information or textual information.

In this paper, we propose a model of CPG therapeutic recommendations that provides attributes for representing the various elements of the textual critique.

For example, if a user was looking for a new science-fiction movie, they might be tempted to follow recommendations to other science-fiction movies based on the title, a brief corresponding textual description or the displayed image.

The ID-control professionals discussed nine recommendations, of which one was found not relevant, seven were found relevant after textual modification and one was found relevant without modification.

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