Cross-Category Product Recommender System based on Multi-Criteria Rating using Diversity and Novelty Evaluation
Saranya Maneeroj, Pongsakorn Jirachanchaisiri, Chanisara Suksomjit, Apirom Zatloukal · 2019
In this paper, we propose a new recommendation method, named “Cross-Category Product with Diversity and Novelty” which uses association rule mining and analytic hierarchy process to recommend a personalized rank list to target users. Long-tail products are also inserted into the list for improving diversity and novelty. The experimental results indicate that our method has higher discounted cumulative gain (DCG), diversity, novelty, and coverage than the research that makes cross-category recommendations on single-criterion rating and the research that uses only collaborative filtering on multi-criteria ratings.