Sentiment Analysis of User Comments for One-Class Collaborative Filtering over TED Talks
| Type of publication: | Conference paper |
| Citation: | Pappas_SIGIR_2013 |
| Publication status: | Accepted |
| Booktitle: | 36th ACM SIGIR Conference on Research and Development in Information Retrieval |
| Year: | 2013 |
| Publisher: | ACM |
| Location: | Dublin, Ireland |
| Abstract: | User-generated texts such as reviews, comments or discussions are valuable indicators of users’ preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by pre-defined rating labels. We investigate their role in a one-class collaborative filtering task such as bookmarking, where only the user action is given as ground-truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly (by more than 25% on unseen data) several competitive baselines. |
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Idiap InEvent |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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