An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

Abstract

A recommender system is an information filtering system that has become a buzzword in various areas of marketing and research such as movies, music, books, products and research articles. The main role of recommender systems is to guide users on a personal level to provide an optimum set of suggestions based on the users’ taste, explicit rating of items, his/her demographic and other related valuable information. In the past decade, several approaches have been discussed for recommendation of items to online users keeping in mind the accuracy of prediction, the cold-start problem and the problem of sparsity. However, most of these existing recommender system techniques have failed to define a proper recommendation model that can be well suited for any online social network and can consider majority of the limitations when modeling real-market recommendations. In this paper, we present a novel efficient recommender system technique RecGyp and many other standard commonly used existing prediction techniques and also perform experimental evaluations to make a comparative analysis among each technique. The experiments carried out on the MovieLens 100K and the Yahoo! Webscope Movie datasets demonstrate the superior nature of the proposed RecGyp technique in solving the scalability issue and accuracy of results for recommending items to users of a social network. In addition to the traditional similarity measurements evaluation, results are also provided for three evaluation metrics, Precision, Recall and ROC, to evaluate the accuracy of all the recommender system techniques

Correlation analysis between talent training quality and regional economic development based on multivariate statistical analysis model

“…In the study about the quality of talent training and regional economic development, more and more experts and scholars have found that the relationship between the two is more complex, and the traditional linear statistical methods have more restrictive conditions to explain the complex nonlinear relationships and the cross-talk between the factors [1][2][3][4]. Machine learning models have strong flexibility and inclusiveness, and there are no strict restrictions on the relationship between the influencing factor and the dependent variable and the distribution state, so relevant studies have been introducing machine learning models to explore the relationship between talent training quality and regional economic development [5][6][7][8]. Random forest is a machine learning method that employs an integrated algorithm, and compared with other machine learning models, random forest overcomes the shortcomings of the traditional variable selection method, which has a randomized nature for the selection of data and features without the need for variable selection, and it also improves the interpretability of the model with the help of methods such as feature importance ranking and biased dependency graph [9][10].…”