Promotional effect on cold start problem and diversity in a data characteristic based recommendation method

TL;DR

This paper introduces a data characteristic based adaptive algorithm for recommendation systems that improves cold start problem handling, enhances diversity, and maintains high accuracy by relating hybridization parameters to item data properties.

Contribution

The paper proposes a novel adaptive hybrid recommendation algorithm that dynamically adjusts parameters based on data characteristics, outperforming existing methods in accuracy and diversity.

Findings

Significant improvement in cold start recommendation accuracy.

Enhanced diversity without sacrificing overall accuracy.

Better adaptation to data characteristics than previous hybrid methods.

Abstract

Pure methods generally perform excellently in either recommendation accuracy or diversity, whereas hybrid methods generally outperform pure cases in both recommendation accuracy and diversity, but encounter the dilemma of optimal hybridization parameter selection for different recommendation focuses. In this article, based on a user-item bipartite network, we propose a data characteristic based algorithm, by relating the hybridization parameter to the data characteristic. Different from previous hybrid methods, the present algorithm adaptively assign the optimal parameter specifically for each individual items according to the correlation between the algorithm and the item degrees. Compared with a highly accurate pure method, and a hybrid method which is outstanding in both the recommendation accuracy and the diversity, our method shows a remarkably promotional effect on the long-standing challenging problem of the cold start, as well as the recommendation diversity, while simultaneously keeps a high overall recommendation accuracy. Even compared with an improved hybrid method which is highly efficient on the cold start problem, the proposed method not only further improves the recommendation accuracy of the cold items, but also enhances the recommendation diversity. Our work might provide a promising way to better solving the personal recommendation from the perspective of relating algorithms with dataset properties.