A Temporal and Social Network-based Recommender using Graph Clustering

Abstract

Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users' community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

“…In terms of comparing this framework to the past related works and particularly aforementioned researches. It can be seen that any prediction model-based frameworks follow the similar steps as thinking of correct dataset for the prediction and utilizing ML algorithms to train the data and then creating a proper model [23]. However, the differences appear with types chosen, number, and accuracy level of each algorithms.…”