Abstract
Twitter has emerged as one of the most powerful micro-blogging services for real-time sharing of information on the web. The large volume of posts in several topics is overwhelming to twitter users who might be interested in only few topics. To this end, we propose TRUPI, a personalized recommendation system for the timelines of twitter users where tweets are ranked by the user’s personal interests. The proposed system combines the user social features and interactions as well as the history of her tweets content to attain her interests. The system captures the users interests dynamically by modeling them as a time variant in different topics to accommodate the change of these interests over time. More specifically, we combine a set of machine learning and natural language processing techniques to analyze the topics of the various tweets posted on the user’s timeline and rank them based on her dynamically detected interests. Our extensive performance evaluation on a publicly available dataset demonstrates the effectiveness of TRUPI and shows that it outperforms the competitive state of the art by 25% on nDCG@25, and 14% on MAP.
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Elmongui, H.G., Mansour, R., Morsy, H., Khater, S., El-Sharkasy, A., Ibrahim, R. (2015). TRUPI: Twitter Recommendation Based on Users’ Personal Interests. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_20
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DOI: https://doi.org/10.1007/978-3-319-18117-2_20
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