Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based Approach

Le résumé fourni par la source

Recent years have witnessed tremendous growth in the publication of research articles as well as in the rise of new research topics. Articles get published in a streaming manner and therefore retrieving and recommending trending topics continuously by updating the trend of topics with time will be beneficial for young researchers. The proposed topic recommendation system is a clustering-based approach that utilizes an autoencoder framework for the generation of clusters. The autoencoder framework considers articles as input in a multiview framework and produces latent data in lower-dimensional space using graph attention-based encoder and decoder networks. The latent data is then partitioned with respect to its different views and finally, a single consensus overlapping partitioning is produced by satisfying all the views. Since the publication of articles is a continuous process, to update the trending topics continuously, the clustering-based approach is kept on and applied iteratively in a sliding window manner. The generated clusters are analyzed to extract the trending topics and recommendations for future scope. An article can belong to multiple topics and the proposed method is developed by considering such criteria and therefore tested with the modified version of the multilabel scientific article data ArXiv, named CSML.ArXiv. The superiority of the proposed method can be observed from its comparisons with existing methods, and a few baseline methods with respect to cluster formation, topic extraction, and trending topic evaluation.

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