1. Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS) Authors: Qiming Diao, Minghui Qiu, Chao-Yuan Wu Presented by Gemoh Mal.

2. Content Introduction Propose model (JMARS) Experiment and Evaluation Conclusion

3. Introduction Collaborative filtering is a huge source on income to many internet businesses. Recommendation and review sites offer a wealth of information beyond ratings. Traditional recommendation systems often use just the ratings of users to make predictions. Data to build good content recommender systems essentially comes in three guises: interactions, ratings, and reviews. This paper present an integrated model that use ratings, reviews, movies, users, to extract a wealth of information (topics and sentiments) to make good recommendation system.

4. JMARS Model Aspect Modeling (Interest of the user/Property of the movie) Rating Modeling Aspect-specific rating Overall rating Rating and Review Model The predicted review rating emphasizes the aspect specific properties.

5. JMARS Model (2) Language model Background: words that are uniformly distributed in every review e.g. movie, film, characters, scenes, real, good. Sentiment: words that do not really convey any aspect specific content such as great, perfect, good, funny, bad, stupid. Movie-specific: any term that appears only in the movie are considered movie-specific. e.g. name of characters, actors, etc. Aspect-specific words associated with specific aspects. Music, sound, singing Aspect-specific sentiment words to express positive or negative sentiments. e.g. Bored, predictable, plot. Each of the language models is a multinomial distribution

6. JMARS Model (3) Topic model Based on decide which of the five model types to pick. If is aspect specific, select φ from the aspect models using aspect If is aspect-sentiment specific, inspect for a matching sentiment for aspect zumi If is sentiment specific, inspect for the corresponding sentiment. How words are generated

7. INFERENCE AND LEARNING ( hybrid sampling/optimization) The goal is to learn the hidden factor vectors, aspects, and sentiments of the textual content to accurately model user ratings and maximize the probability of generating the textual content. They use Gibbs-EM, an inference method that alternates between collapsed Gibbs sampling and gradient descent, to estimate parameters in the model. Objective function consist of two parts. – The first term denotes the prediction error on user ratings. – The second term denotes the probability of observing the text conditioned on priors. E-step, here they perform Gibbs sampling to learn the hidden variables by fixing the values of and M-step, here they perform gradient descent to learn hidden factor vectors by fixing the values of

8. Experiment and Evaluation Dataset compiled from IMDb. They use 80% of the dataset as training data, 10% for validation, and 10% for testing. Movie recommendation

9. Perplexity Perplexity is a standard measure to evaluate the quality of probabilistic models. The performance in terms of perplexity shows the prediction power of the model on unseen reviews, where a lower perplexity means a better performance.

10. ‘Cold start’ recommendation Improvement in MSE compared to PMF for ‘cold-start’ movies and users. This shows the benefit of modeling review texts for recommendation. Using information from the review helps to tackle the cold start problem

11. QUALITATIVE EVALUATION Aspect rating, Sentiment & Movie-Specific Words To evaluate if the model is capable of interpreting the reviews correctly, they examine the learned aspect ratings.

12. Conclusion This paper proposed a Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS) which provides superior recommendations by exploiting all the available data sources. They involve information from review and ratings. This model is able to capture the sentiment in each aspect of a review, and predict partial scores under different aspects. Additionally the user interests and movie topics can also be inferred with the integrated model. The model outperforms state-of-the-art systems in terms of prediction accuracy and the language model for reviews is accurate.