摘要
目前基于哈希技术的推荐算法常用汉明距离表示用户和项目哈希码的相似性,但忽略了哈希码中每位的潜在区别信息,为此提出了一个差异性汉明距离,通过考虑哈希码之间的差异性为哈希码赋予位权重;为差异性汉明距离设计了一个变分推荐模型,该模型分为用户哈希组件和项目哈希组件两部分,以变分自编码器结构连接。首先,模型利用编码器为用户和项目生成哈希码,为提高哈希码的鲁棒性,在哈希码中加入高斯噪声。其次,通过差异性汉明距离优化用户和项目哈希码,以最大限度地提高模型重构用户-项目评分的能力。在两个公开的数据集上的实验结果表明,在计算开销不变的前提下与最先进的哈希推荐算法相比,所提模型在NDCG上提高了3.9%,在MRR上提高了4.7%。
Current recommendation algorithms based on hashing technology commonly uses Hamming distance to indicate the similarity between user hash code and item hash code,while it ignores the potential difference information of each bit dimension.Therefore,this paper proposes a differential Hamming distance,which by calculating the dissimilarity between hash codes to assign bit weights.This paper designs a variational recommendation model for dissimilarity Hamming distance.The model is divided into a user hash component and an item hash component,which are connected by variational autoencoder structure.The model uses encoder to generate hash codes for user and items.In order to improve the robustness of the hash codes,we apply a Gaussian noise to both user and item hash coeds.Besides,the user and item hash codes are optimized by differential Hamming distance to maximize the ability of the model to reconstruct user-item scores.Experiments on benchmark datasets demonstrate that the proposed algorithm VDHR improves 3.9%in NDCG and 4.7%in MRR compared to the state-of-the-art hash recommendation algorithm under the premise of constant computational cost.
作者
董家玮
孙福振
吴相帅
吴田慧
王绍卿
DONG Jia-wei;SUN Fu-zhen;WU Xiang-shuai;WU Tian-hui;WANG Shao-qing(School of Computer Science and Technology,Shandong University of Technology,Zibo,Shandong 255000,China)
出处
《计算机科学》
CSCD
北大核心
2022年第12期178-184,共7页
Computer Science
基金
国家自然科学基金(61841602)
山东省自然科学基金(ZR2020MF147)。
关键词
汉明距离
差异性汉明距离
位权重
推荐算法
变分自编码器
Hamming distance
Differential Hamming distance
Bit weights
Recommendation algorithm
Variational autoencoder
