A Collaborative Filtering Algorithm based on Citation Information

Abstract

Objective: With the rapid growing number of published scientific papers in the age of big data, users often find themselves difficult to select useful information from such massive academic information. This paper aims at the problems of collaborative filtering techniques in scientific citation data. Methods: This paper proposes an improved machine learning algorithm, that is designed to predict user ratings of academic theses by using Fisher Linear Regression combined with information of confidence scores, preference scores, the number of cooperative users in science citation data and the actual citation scores. Results: Multiple features considered in the algorithm have a positive impact on the recommendation results. Conclusion: This paper proposed a collaborative filtering recommendation algorithm combined with citation data analysis in order to improve the accuracy of predicted results. The experiments have shown that the proposed algorithm is proved to be effective and improve the accuracy of recommendation.

Fuzzy Tools in Recommender Systems: A Survey

“…In the equations, ε is a stop threshold, m is a fuzziness exponent, and || * || is a norm expressing the similarity between any measured datum and the centroid. Regarding both ratings and additional items' information, several works have employed the fuzzy c-means clustering [11,38,40,66,73,87,101,103,128,129,131,141,142], and also similar approaches such as relational fuzzy subtractive clustering [121], co-clustering [45,49,114,133], picture fuzzy clustering [123], folksonomy-focused intuitionistic fuzzy agglomerative hierarchical clustering [43], fuzzy geographical clustering [119], linear fuzzy clustering [48], and other fuzzy clustering approaches [18,29,35,39,61,64,143].…”