Joint Representation Learning Based On Multi-source Data And Its Management Application

Multi-source data exist widely in today's society,which include data generated by different entities,such as user-generated content,enterprise-generated content,etc.? they also include data generated by different data generators in different forms,such as images,audio,text,etc.? and they also include data from different domains,such as beauty,home,electronics and other various categories in e-commerce platforms.Multi-source data can provide us with a more comprehensive view to help us get better predictions and better solve management problems.At the same time,they make our forecasts more robust because it avoids data omissions,errors,and other problems.But multi-source data also bring us challenges as well.Multiple sources of data make it time-consuming and labor-intensive for us to collect,explore and utilize them.While multi-source data bring more burden to storage,computation,etc.,there is no guarantee that they can bring better results and generate higher value for the management context.And they may even bring negative effects.Therefore,the research question of this paper is: How to design a framework for handling multi-source data to solve the above chal-lenges? For this purpose we propose a framework for multi-source data learning.In the framework,we classify multi-source data learning into two types: multi-source data fu-sion and multi-source data transfer,i.e.,we will address the above challenges from these two types respectively.Multi-source data fusion is used to solve the problem of fusion of multiple infor-mation sources.Its related traditional approaches take joint representation,coordinated representation,etc.,and these models have difficulty in finding the connection between high-dimensional and complex data.In addition,different task scenarios require different connections,and it is an important challenge to filter different connections for different tasks.Therefore,we propose a model based on encoder-decoder and attention mechanism to solve the problem of multi-source data fusion.The proposed model can effectively learn the connections between different data sources by generating data sources from each other,then use the attention mechanism to filter the task-related connections,thus significantly improve the model's representation and prediction capabilities.We then demonstrate the superiority and effectiveness of our model in solving multi-source data fusion in a business role prediction management scenario.We also effectively solve the management problem of small and medium-sized foreign trade enterprises searching for product-related partners through search engines.Multi-source data transfer is used to solve the transfer of information between dif-ferent data sources.One problem of traditional methods is that although some models are able to learn the common features of different data sources to obtain some better general-ization ability,they tend to ignore the internal features of the data sources,which makes the models' performance on new data sources drop more substantially.In this paper,we propose a model based on adversarial mechanism and graph at-tention network to solve the problem of multi-source data transfer.The proposed model learns the common features of different data sources through adversarial game to obtain strong transfer ability.At the same time,the model learns the unique features within the data sources through graph attention networks to further improve the performance of the model.Then we demonstrate the superiority and effectiveness of the model in solving the set-based multi-source data transfer in the management scenario of fake news detection,and our work also effectively solves the management problem of fake news detection.Fi-nally,we demonstrate the superiority and effectiveness of the model in solving the problem of distribution-based multi-source data transfer in the management scenario of recommen-dation systems.Our work also advances the related research on recommendation systems.