Abstract：The existing recommender systems still face challenges below，resulting in less than satisfactory user experiences.They have overlooked the fact that user preference and item attribute change over time.Moreover，they provide improvement in accuracy usually at the expense of diversity and novelty.In this direction，we propose multiple objective interactive recommender systems which can better balance the conflicts in diversity，novelty and accuracy metrics and adapt to changes of user preference and item attribute.The models rely on three main components：multi-objective optimization functions built by the methods of ideal points，dynamic prioritization schemes for weighting quality metrics and recommendation technologies modeled by the multi-armed bandit algorithm.The experimental results show that the proposed algorithms provide the capability to respond to a change in user requirements in real time，and recommend lists of personalized items that are accurate，diverse and novel.
何炜俊,艾丹祥. 以多臂赌博机建模的多目标互动式推荐系统[J]. 小型微型计算机系统, 2021, 42(6): 1192-1198.
HE Wei-jun,AI Dan-xiang. Multiple Objective Interactive Recommender Systems Based on Multi-armed Bandits. Journal of Chinese Computer Systems, 2021, 42(6): 1192-1198.
2021, Vol. 42 
