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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).
Cosine similarity is one of the commonly adopted similarity measures to determine the nearest neighbour in recommendation generation.
We assume all users participate to calculate averages and similarities among the items in the system and only one user (target user) participates in recommendation generation.
Similar(51)
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, 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.
Therefore, user's ratings as well as the recommendation results are secure during CF-based recommendations generation. .
Once the items' averages and similarities are calculated, one user ("target user") is randomly assigned for recommendations generation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com