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Wikipedia enriched advertisement recommendation for microblogs by using sentiment enhanced user profiles
Date
2020-04-01
Author
Simsek, Atakan
Karagöz, Pınar
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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Advertisement recommendation on the Web is a popular research problem. For microblog platforms, different requirements arise due to the differences in the context of social media and social network. In this work, we propose an advertisement recommendation technique for microblogs. The proposed solution uses all contents of the messages (texts, captions, web links, hashtags), and enhances them with sentiment data and followee/follower interactions expressed as microblog posts to generate a new user model. As another novel feature, Wikipedia Good Pages are used as general background knowledge for matching user profiles and advertisement contents. On the basis of the similarity between advertisement vectors and user profile vectors, the most related advertisement for the selected user is determined. Evaluation results show that the proposed solution performs better for advertisement recommendation on microblog platform and works faster in comparison to other techniques.
Subject Keywords
Twitter
,
Mechanism
,
Information
,
Short text
,
Social media
,
Matching approach
,
Word-of-mouth
URI
https://hdl.handle.net/11511/32744
Journal
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
DOI
https://doi.org/10.1007/s10844-018-0540-5
Collections
Department of Computer Engineering, Article