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Neural Serendipity Recommendation
ACM Transactions on Knowledge Discovery from Data ( IF 3.6 ) Pub Date : 2020-06-16 , DOI: 10.1145/3396607
Yuanbo Xu 1 , Yongjian Yang 1 , En Wang 1 , Jiayu Han 1 , Fuzhen Zhuang 2 , Zhiwen Yu 3 , Hui Xiong 4
Affiliation  

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity , which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

中文翻译:

神经偶然性推荐

推荐系统在向用户提供个性化信息方面一直发挥着重要作用。然而,推荐系统的准确性和新颖性之间总是需要权衡取舍。通常,许多用户都遭受冗余或不准确的推荐结果的困扰。为此,在本文中,我们努力探索观察评级的隐藏知识,以缓解这种推荐困境。具体来说,我们利用一些基本概念来定义一个概念,机缘巧合,其特点是高满意度和低初始兴趣。基于这个概念,我们提出了一个两阶段推荐问题,旨在通过偶然性预测和个性化推荐在准确性和新颖性之间取得平衡。沿着这条线,首先通过结合多层感知器和矩阵分解来开发神经意外发现推荐 (NSR) 方法来进行意外发现。然后,设计了一种加权候选过滤方法用于个性化推荐。最后,对真实世界数据的广泛实验表明,与最先进的方法相比,NSR 可以通过平均提高 12% 实现卓越的意外发现,同时保持稳定的准确性。
更新日期:2020-06-16
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