Personal Tastes vs. Fashion Trends: Predicting Ratings Based on Visual Appearances and Reviews

People have their own tastes on visual appearances of products from various categories. For many of them, the tastes are affected by the current fashion trend. Studying visual appearances and fashion trend makes us understand the composition of users' preferences and their purchase choices. However, since the fashion is changing over time, it is complex to model time-aware and non-time-aware variables simultaneously. In this paper, we present VIsually-aware Temporal rAting modeL with topics using review text to help mine visual dynamics and non-visual features for rating prediction task. Understanding the reviews will help the Recommender Systems (RSs) know whether a user is attracted by the appearance of an item, and which aspect of an item's appearance contributes most to its ratings. To achieve this, we incorporate the visual information into the rating predicting function and introduce a topic model that can automatically classify words in an item's reviews into non-visual words that explain the coherent feature, and visual words that are associated with its visual appearances in each time period, respectively. We run experiments on eleven real-world public datasets and the results show that our model performs better on predicting ratings than many of the state-of-the-art RSs, such as PMF, timeSVD++, HFT, JMARS, ETDR, and TVBPR+.