Download PDFOpen PDF in browserData Integration and Preprocessing Techniques for Researcher Recommendation SystemsEasyChair Preprint 1406818 pages•Date: July 21, 2024AbstractResearcher recommendation systems have become essential tools for facilitating collaboration, promoting knowledge sharing, and enhancing academic productivity. One of the critical challenges in building effective researcher recommendation systems is the integration and preprocessing of diverse and heterogeneous data sources. This abstract overviews data integration and preprocessing techniques employed in researcher recommendation systems.
Data integration involves gathering data from various sources such as academic databases, research publications, and collaboration networks. Techniques like web scraping, APIs, and data feeds are employed to extract and collect relevant data. Data cleaning processes, including duplicate removal, standardization of data formats, and handling missing data, are crucial for ensuring data quality and consistency. Furthermore, data transformation and merging techniques like normalization, entity resolution, and data fusion are used to reconcile and combine data from different sources.
Preprocessing the integrated data is essential for effective recommendation system algorithms. Text preprocessing techniques such as tokenization, stop word removal, stemming, and lemmatization are applied to extract meaningful features from textual data. Feature extraction methods like bag-of-words representation, TF-IDF, and word embeddings help capture the semantic meaning and context of the research content. Dimensionality reduction techniques like PCA, SVD, and t-SNE are employed to reduce the high-dimensional feature space and improve computational efficiency. Additionally, data discretization and scaling techniques like binning, min-max scaling, and z-score normalization are utilized to normalize and standardize numerical features. Keyphrases: Data Scaling, Equal Width Binning, Equal-frequency binning, Preprocessing techniques, data discretization
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