Diversified Recommendation Incorporating Item Content Information Based on MOEA/D
Jinkun Wang, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, Chunhua Sun · 2016
There has been an increasing awareness that accuracy is not the only criteria in the evaluation of recommender systems. Additional properties such as diversity, novelty and interpretability are playing more important roles in increasing satisfaction of users when interacting with the recommender systems. However, designing a recommendation algorithm that optimizes the abovementioned properties simultaneously is hard since these objectives are conflicting. In this paper, we propose a multi-objective evolutionary algorithm based on decomposition to recommend diversified recommendation lists to each user. Notably, the item content information are taken into account when devising the diversity objective function, which makes the recommendation lists highly explainable. Experimental results on the movie dataset demonstrate that the proposed algorithm can generate a more diversified and novel recommendation, without sacrificing the accuracy significantly.