Diversified Recommendation Incorporating Item Content Information Based on MOEA/D
Diversified Recommendation Incorporating Item Content Information Based on MOEA/D
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DOI：
10.1109/hicss.2016.91
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Jinkun Wang;Yezheng Liu;Jianshan Sun;Yuanchun Jiang;Chunhua Sun
中科院分区：
文献类型：
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作者：
Jinkun Wang;Yezheng Liu;Jianshan Sun;Yuanchun Jiang;Chunhua Sun
人们越来越意识到，准确性不是评价推荐系统的唯一标准。其他属性，如多样性，新奇和可解释性在与推荐系统交互时，在提高用户满意度方面发挥着越来越重要的作用。然而，设计一个推荐算法，同时优化上述属性是困难的，因为这些目标是相互冲突的。在本文中，我们提出了一个多目标的进化算法的基础上分解推荐多样化的推荐列表给每个用户。值得注意的是，在设计多样性目标函数时考虑了项目内容信息，这使得推荐列表具有高度的可解释性。在电影数据集上的实验结果表明，该算法在不显著牺牲推荐准确率的前提下，能够生成更多样化、更新颖的推荐结果。
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.