Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 33, Issue 9, Pages 3160-3172Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2020.2966971
Keywords
Recommender systems; Sentiment analysis; Context modeling; Task analysis; Machine learning; Support vector machines; Fuses; Emoji recommendation; matrix factorization; personalization; recommender system
Categories
Funding
- NSFC [61902309, 61732008, 61772407, 1531141]
- National Key RD Program of China [2017YFF0107700]
- World-Class Universities
- Characteristic Development Guidance Funds for the Central Universities [PY3A022]
- National Postdoctoral Innovative Talents Support Program
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In this paper, a Context-Aware Personalized Emoji Recommendation (CAPER) model is proposed, which fuses contextual and personal information to improve recommendation accuracy. Experimental results show better performance of the CAPER model compared to existing methods, demonstrating the effectiveness of considering contextual and personal factors.
With the popularity of social platforms, emoji appears and becomes extremely popular with a large number of users. It expresses more beyond plaintexts and makes the content more vivid. Using appropriate emojis in messages and microblog posts makes you lovely and friendly. Recently, emoji recommendation becomes a significant task since it is hard to choose the appropriate one from thousands of emoji candidates. In this paper, we propose a Context-Aware Personalized Emoji Recommendation (CAPER) model fusing the contextual information and the personal information. It is to learn latent factors of contextual and personal information through a score-ranking matrix factorization framework. The personal factors such as user preference, user gender, and the current time can make the recommended emojis meet users' individual needs. Moreover, we consider the co-occurrence factors of the emojis which could improve the recommendation accuracy. We conduct a series of experiments on the real-world datasets, and experiment results show better performance of our model than existing methods, demonstrating the effectiveness of the considering contextual and personal factors.
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