A genetic algorithm solution to the collaborative filtering problem

作者：Ar Yilmaz^*; Bostanci Erkan

来源：Expert Systems with Applications, 2016, 61: 122-128.

DOI：10.1016/j.eswa.2016.05.021

• Collaborative filtering
• Genetic algorithms
• Evaluation
• Recommender systems

Development of approaches for reducing the prediction error has been an active research field in collaborative filtering recommender systems since the accuracy of the prediction plays a crucial role in user purchase preferences. Unlike the conventional collaborative filtering methods which directly use the computed user-to-user similarity values, this paper presents a genetic algorithm approach for refining them before using in the prediction process. The approach was found to yield promising results according to the statistical analysis performed on a variety numbers of neighbours for various similarity metrics including Pearson's Correlation, Extended Jaccard Coefficient and Vector Cosine Similarity along with a metric that assigns random weights to be used as a benchmark. Results show that the evolutionary approach has significantly reduced the prediction error using the evolved weights and Vector Cosine Similarity has shown the best performance.

• 出版日期2016-11-1