Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/2623330.2623758

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