Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2014
Abstract
Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings. The ability to answer questions such as “Does this user care more about the plot or about the special effects?” or ”What is the quality of the movie in terms of acting?” helps us to understand why certain ratings are generated. This can be used to provide more meaningful recommendations. In this work we propose a probabilistic model based on collaborative filtering and topic modeling. It allows us to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on a per-aspect basis. Unlike prior work our approach is entirely unsupervised and does not require knowledge of the aspect specific ratings or genres for inference. We evaluate our model on a live copy crawled from IMDb. Our model offers superior performance by joint modeling. Moreover, we are able to address the cold start problem — by utilizing the information inherent in reviews our model demonstrates improvement for new users and movies.
Keywords
Collaborative Filtering, Topic Models, Integrated Modeling, Sentiment Analysis
Discipline
Computer Sciences | Databases and Information Systems
Publication
KDD '14: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: August 24-27, 2014, New York
First Page
193
Last Page
202
ISBN
9781450329569
Identifier
10.1145/2623330.2623758
Publisher
ACM
City or Country
New York
Citation
DIAO, Qiming; QIU, Minghui; WU, Chao-Yuan; SMOLA, Alexander J.; JIANG, Jing; and WANG, Chong.
Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS). (2014). KDD '14: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: August 24-27, 2014, New York. 193-202.
Available at: https://ink.library.smu.edu.sg/sis_research/2415
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1145/2623330.2623758