Abstract
In social based web services systems, some resources gain popularity while others do not. It would be valuable if we can predict the popularity of certain resource. In this work, we study the recipe popularity prediction problem using the Yelp dataset. We investigate various features that can be extracted and help to improve the performance. In particular, we propose to do the sentiment analysis over the reviews and treat the sentimental scores as one of the features. A polynomial regression model is developed to predict the recipe popularity. The experimental results show that our proposed method outperforms the baseline method.
Original language | English |
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Pages | 568-572 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 1 Jan 2013 |
Externally published | Yes |
Event | 2013 International Joint Conference on Awareness Science and Technology, iCAST 2013 and 6th International Conference on Ubi-Media Computing, UMEDIA 2013 - Aizuwakamatsu, Japan Duration: 2 Nov 2013 → 4 Nov 2013 |
Conference
Conference | 2013 International Joint Conference on Awareness Science and Technology, iCAST 2013 and 6th International Conference on Ubi-Media Computing, UMEDIA 2013 |
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Country/Territory | Japan |
City | Aizuwakamatsu |
Period | 2/11/13 → 4/11/13 |
Keywords
- Popularity prediction
- Regression
- Sentiment analysis
- Social network
ASJC Scopus subject areas
- Software