This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are tr...This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.展开更多
In the era of information technology,recommendation systems play a crucial role in information filtering and user preference identification.Notably,the auxiliary information provided by online social platforms offers ...In the era of information technology,recommendation systems play a crucial role in information filtering and user preference identification.Notably,the auxiliary information provided by online social platforms offers significant support for enhancing the performance of recommendation systems.Based on the hypothesis that socially connected users share similar preferences,inte-grating social relationships as supplementary information into recommendation algorithms can significantly enhance recommendation accuracy while effectively mitigating the cold-start prob-lem.However,existing social recommendation systems primarily rely on explicit social relation-ships as auxiliary information,often overlooking the value of potential social connections.Research indicates that users with potential social links may also possess valuable preference information.We believe that mining potential social relationships can provide valuable auxiliary information,thereby enhancing the performance of recommendation systems.To address this issue,we propose a social recommendation model based on social semantic mining and denoising(SSMD).Specifically,we propose an encoder-decoder architecture to learn explicit social user representations and mine potential social relationships.Considering the potential noise in these implicit connections,we design a denoising module that utilizes user preference information to filter unreliable social links.Furthermore,we implement cross-view information alignment be-tween the potential social graph and interaction graph through an auxiliary loss function.Extensive experiments conducted on multiple public datasets demonstrate that our SSMD method outperforms various baseline approaches with significant improvements.展开更多
基金Supported by the National Basic Research Program of China 973 Program (2007CB310801)the Specialized Research Fund for the Doctoral Program of Higer Education of China (20070486064)+1 种基金the Natural Science Foundation of Hubei Province (2007ABA038)the Programme of Introducing Talents of Discipline to Universities (B07037)
文摘This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.
基金supported by the National Natural Science Foundation of China(62077038,61672405,62176196 and 62271374).
文摘In the era of information technology,recommendation systems play a crucial role in information filtering and user preference identification.Notably,the auxiliary information provided by online social platforms offers significant support for enhancing the performance of recommendation systems.Based on the hypothesis that socially connected users share similar preferences,inte-grating social relationships as supplementary information into recommendation algorithms can significantly enhance recommendation accuracy while effectively mitigating the cold-start prob-lem.However,existing social recommendation systems primarily rely on explicit social relation-ships as auxiliary information,often overlooking the value of potential social connections.Research indicates that users with potential social links may also possess valuable preference information.We believe that mining potential social relationships can provide valuable auxiliary information,thereby enhancing the performance of recommendation systems.To address this issue,we propose a social recommendation model based on social semantic mining and denoising(SSMD).Specifically,we propose an encoder-decoder architecture to learn explicit social user representations and mine potential social relationships.Considering the potential noise in these implicit connections,we design a denoising module that utilizes user preference information to filter unreliable social links.Furthermore,we implement cross-view information alignment be-tween the potential social graph and interaction graph through an auxiliary loss function.Extensive experiments conducted on multiple public datasets demonstrate that our SSMD method outperforms various baseline approaches with significant improvements.
