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08.12.20184

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Such so- lutions are either heuristic approaches or machine learn- ing based approaches with auto-generated training data. For example, Kong, Zhang, and Yu con- vert the attribute value text into bag-of-words vectors with TF-IDF weights, and then compute the similarity with the inner product and the cosine similarity. The process of sampling negative examples is not included in Algorithm 1.

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Researchers have proposed unsupervised approaches for the UIL problem which requires no labelled data. The variations follow differ- ent patterns, such as acronyms, abbreviations, synonyms and translations, in different social networks. First, it automatically captures the word-level mapping and sequence-level mapping with almost no feature engineering; Second, it only requires positive examples aligned attribute pairs as training data which relaxes the effort of sampling negative examples.

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Comments (4)

  1. Linking identical users across different so- cial networks, also known as the User Identity Linkage UIL problem, is fundamental for many applications. We further compare CoLink with the state-of-the- art unsupervised approaches.

  2. Traditional string similarity functions can only cover some patterns, but never all.

  3. We summa- rize our contributions as follows. Bilenko and Mooney proposed a SVM based similarity which can be learned from bag-of-words vectors of matched string pairs.

  4. CoLink employs a co-training algorithm, which manipu- lates two independent models, the attribute-based model and the relationship-based model, and makes them reinforce each other iteratively in an unsupervised way. The performance very much depends on the quantity and quality of the auto-generated training data which is sensitive to the targeted UIL tasks.

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