Outlier item detection in bundle recommendation via the attention mechanism  

作　　者：Yuan Liang 

机构地区：[1]School of Information Engineering,Suqian University,Suqian 223800,China [2]Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China

出　　处：《High-Confidence Computing》2024年第3期51-57,共7页高置信计算（英文）

基　　金：supported in part by the Guangxi Key Laboratory of Trusted Software,China(KX202037);the Project of Guangxi Science and Technology,China(GuiKeAD 20297054);the Guangxi Natural Science Foundation Project,China(2020GXNSFBA297108)。

摘　　要：Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once.However,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation algorithms.Furthermore,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a bundle.Identifying them can aid users in refining their item combinations.While the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning phase.This limitation can hinder the algorithm’s performance.To tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item detection.The encoder learns potential item correlations through a self-attention mechanism.Concurrently,the decoder garners efficient inference frameworks by directly assessing item anomalies.We have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.

关 键 词：Bundle recommendation Outlier item detection Attention mechanism Decoder encoder 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]