|Place of Conferral||北京|
|Keyword||公共数字文化资源 Lda 个性化推荐 协同过滤推荐 标签融合 时间加权|
|Other Abstract||Public digital cultural resources are multi-source heterogeneous data. People can hardly find the resources they are interested. The purpose of personalized recommendation is to capture user interest in real time and actively recommend favorite resources to the user. Based on public digital cultural resources, this paper mainly uses semantic methods to analyze resources, further optimizes personalized recommendation algorithms and applies them to public digital cultural resource platforms. According to the characteristics of public digital cultural resource tags and different recommendation algorithms, two new recommendation algorithms based on collaborative filtering are proposed to help solve the problems of user interest changes over time and data sparsity in collaborative filtering algorithms. The main work and achievements are as follows:|
Firstly, public cultural resources was researched. At the same time, the advantages and disadvantages of different commonly appling recommendation algorithms are compared. By considering the characteristics of public cultural resources, collaborative filtering recommendation algorithm is selected as the basic algorithm of the research precision recommendation of public digital culture resources.
Secondly, this work has implemented the extraction of semantic tags from a small amount of metadata of public digital cultural resources. The technology includes a meta-information thematic analysis algorithm of cultural resources based on the LDA model, extracting semantic tags; and the Word2Vec algorithm based on the deep neural network, extending the semantic tags of resources to build a tag library of cultural resources, and provides the basis for optimization of personalized recommendation algorithms.
Thirdly, based on the public digital cultural platform, two new recommendation methods are proposed. One is collaborative filtering recommendation combined with tags, which obtains low-dimensional spatial data by constructing user-label scores to help solves the problem of data sparsity; the other one is a time-weighted collaborative filtering recommendation algorithm, which determines the user time-weighted coefficient by introducing an exponential function that decreases over time, and then adjusts the user-resource rating matrix to recommend and help solve the problem of user interest changes over time
Finally, this work has developed a personalized recommendation system based on public digital cultural resources, and implemented the system's input and output modules, model analysis module, recommendation engine module and model evaluation module. And for the public digital digital sharing service platform, the API interface has been designed to provide personalized recommendation analysis, supporting the platform to recommend digital cultural resources of interest for users.
In this thesis, the LDA model and Word2Vec model in the field of natural language processing are used to semantically analyze the public digital cultural resources. The analysis results are integrated into the recommendation algorithm, and a personalized recommendation algorithm based on tag fusion and time-based weighting is proposed. And the accuracy and effectiveness are verified by experiments to provide an effective and credible technical approach for the analysis of public cultural resources and platform optimization, which is of great significance for promoting the construction of public cultural resources.
|叶墅锋. 公共数字文化平台资源个性化推荐技术研究[D]. 北京. 中国科学院研究生院,2018.|
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