Diversification of recommendations through semantic clustering

作者：Borras Joan^*; Moreno Antonio; Valls Aida

来源：Multimedia Tools and Applications, 2017, 76(22): 24165-24201.

DOI：10.1007/s11042-016-4166-8

• Clustering
• Decision support
• Knowledge personalization and customization
• Similarity measures

Recommender systems aim to suggest lists of items that match accurately the user's preferences. In the last years it has been argued that the diversity of the recommendations also plays an important role in the overall satisfaction of the user. Increasing the diversity of the suggestions may be beneficial both for the user and for retailers. This paper provides a brief review of the most popular diversification mechanisms and it introduces two new ones (Cluster Random and Cluster Quadratic) based on the semantic clustering of the domain objects. It also shows how the level of diversification may be dynamically adapted to the variety in the preferences of the user. A thorough evaluation of the diversification mechanisms on a Tourism recommender has been performed, reaching the conclusion that the new Cluster Quadratic diversification method achieves very competitive levels of precision and recall, while keeping an acceptable computational cost.

• 出版日期2017-11