Unsupervised Qualitative Scoring for Binary Item Features The qualitative score of tags is widely used to describe which product is better in terms of the given property. For example, in a restaurantnavigation site, properties such as food, location, and mood are given in the form of numerical values, representing the goodness of each aspect. In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products. Based on a natural assumption that an item with a better property is more popular among users who prefer that property, in short, experts know best, we introduce one discriminative and two generative models with which user preferences and itemqualitative scores are inferred from useritem interactions. Our approach contributes to resolving the following difficulties: (1) no supervised data for the score estimation, (2) implicit user purpose, and (3) irrelevant tag contamination. We evaluate our models by using two artificial datasets and two realworld datasets of movie and book ratings and observe that our models outperform baseline models.

Personalized Neural Usefulness Network for Rating Prediction Reviews are crucial to rating prediction on ecommerce websites such as Amazon and Yelp. They reflect user preferences and item properties. Considering not all parts of the reviews are equally important for rating prediction, some works select the more informative words of reviews, others focus on finding the more useful reviews for each user and item. However, on the one hand, the meaning of words may be incomplete, further they may distort the original meaning of sentences, on the other hand, for the more useful reviews, they still contain more useful parts and less useful parts for rating prediction. To address these problems, we propose to model reviews in a view of sentence usefulness, since different sentences have varied importance for reviews modeling. In addition, the preferences of a user may vary with different item properties, it leads to the inconstant usefulness of a user sentence for different items, and vice verse. Hence, in this paper, we show a Personalized Neural Usefulness Network (PUN) to capture the varied sentence usefulness of reviews for rating prediction. Expensive experiments on benchmark datasets demonstrate that PUN significantly outperforms the stateoftheart baseline models. In the meantime, according to personalized sentence usefulness, PUN also attains highlyuseful sentences to better understand the varied user preferences and item properties.