Getting What You Want: Accurate Document Filtering in a Terabyte World

Seed-Guided Topic Model for Document Filtering and Classification

ACM Transactions on Information Systems ◽

10.1145/3238250 ◽

2019 ◽

Vol 37 (1) ◽

pp. 1-37 ◽

Cited By ~ 6

Author(s):

Chenliang Li ◽

Shiqian Chen ◽

Jian Xing ◽

Aixin Sun ◽

Zongyang Ma

Keyword(s):

Topic Model ◽

Document Filtering

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User-Interest-Based Document Filtering via Semi-supervised Clustering

Lecture Notes in Computer Science - Foundations of Intelligent Systems ◽

10.1007/11425274_59 ◽

2005 ◽

pp. 573-582 ◽

Cited By ~ 1

Author(s):

Na Tang ◽

V. Rao Vemuri

Keyword(s):

User Interest ◽

Supervised Clustering ◽

Document Filtering

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User Profiles for Personalizing Digital Libraries

Handbook of Research on Digital Libraries ◽

10.4018/978-1-59904-879-6.ch015 ◽

2009 ◽

pp. 149-158 ◽

Cited By ~ 7

Author(s):

Giovanni Semeraro ◽

Pierpaolo Basile ◽

Marco de Gemmis ◽

Pasquale Lops

Keyword(s):

Machine Learning ◽

Digital Libraries ◽

Machine Learning Techniques ◽

Linguistic Knowledge ◽

User Profiles ◽

User Interests ◽

Learning Techniques ◽

Document Filtering ◽

The Individual ◽

Machine Readable

Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.

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