Sentence examples for generating the recommendations from inspiring English sources

Most recommendations engines pre-compute stuff rather than generating the recommendations in real-time like we do," Directed Edge co-founder Scott Wheeler tells us.

In the following, we present this systematic literature review, which served as the background for generating the recommendations document.

4 The objective of the current work was to review systematically the available literature concerning one of the 10 selected questions as an evidence base for generating the recommendations.

Only asthma drugs that were (1) prescribed and dispensed within 1 year of the index date and (2) active (ie, based on prescription algorithms, it is likely that the person has a supply of the medication) or expired within 30 days prior to the index date were considered when generating the recommendations.

In order to protect against breaches of personal information, it is necessary to obfuscate the user information by means of an efficient encryption technique while simultaneously generating the recommendation by making true information inaccessible to the system.

Settings To generate the recommendations, we assume there is only one user (u_i) (this could be a new user or from the set of n users, given that he has provided ratings on same set of m items) who has requested for recommendations.

"Because we're doing this [pre-caching] with immediacy – we're taking things in the morning for the day ahead — it allows us to be a bit smarter," says Sandler. "Right now it's just we're using information from our partners [to generate the recommendations] — seeing what's popular on these services and indexing accordingly.

Furthermore, the larger number of users in the IMDb data set leads to a substantially smaller fraction of possible co-ratings between items being present, thus making it more difficult for the association rules, collaborative filtering, and interpolation weights algorithms, which rely on co-ratings to generate the recommendations.

In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations.

In particular, intermediaries play a crucial role in how consumers evaluate a health recommendation system, over and above the information system that is used to generate the recommendations.

GA-Z and RR drafted the first version of the manuscript, and WS commented on this version, with all authors helping to revise the article accordingly and generate the recommendations.