Sentence examples for recommendations generation from inspiring English sources

Security in Recommendations Generation (a) CBF-Based Recommendations To generate recommendations in CBF, target user encrypts item preferences using own public key (y_i) and sends them to the server.

Therefore, user's ratings as well as the recommendation results are secure during CF-based recommendations generation.    .

Therefore, the computational cost for a similarity calculation does not affect the efficiency of recommendations generation.

Secondly, we represent privacy-preserving recommendations generation as shown in Fig. 2b.

Once the items' averages and similarities are calculated, one user ("target user") is randomly assigned for recommendations generation.

Server performs homomorphic operation to calculate average and similarity, thereby stores the results in its own database, b recommendations generation, only one user participates and send the ciphertexts of own ratings.

We propose different privacy protocols for item average and similarity computations as well as recommendations generations by which the privacy of users is preserved.

Recommendation generation module To generate recommendations, association rule mining [34] is used.

PLORS has been designed to be integrated in any LMS and consists of four modules that gather information about learners (Learner Modelling Module), create neighborhoods (Neighborhood generation module), generate recommendations (recommendation generation module), and display recommendations to learners (Recommendation display module).

The proposed method does not have any accuracy loss during recommendation generation.

For instance, we show the process of private CBF-based recommendation generation for user (u_2) on item (i_1).