A Joint Summarization and Pre-Trained Model for Review-Based Recommendation

Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large number of reviews, it has become very important to extract useful information from reviews. Automatic summarization can capture important information from a set of documents and present it in the form of a brief summary. Therefore, integrating automatic summarization into recommendation systems is a potential approach for solving this problem. Based on this idea, we propose a joint summarization and pre-trained recommendation model for review-based rate prediction. Through automatic summarization and a pre-trained language model, the overall recommendation model learns a fine-grained summary representation of the key content as well as the relationships between words and sentences in each review. The review summary representations of users and items are finally incorporated into a neural collaborative filtering (CF) framework with interactive attention mechanisms to predict the rating scores. We perform experiments on the Amazon dataset and compare our method with several competitive baselines. Experimental results show that the performance of the proposed model is obviously better than that of the baselines. Relative to the current best results, the average improvements obtained on four sub-datasets randomly selected from the Amazon dataset are approximately 3.29%.

Color Variability Judgments in Peripheral Vision: Evidence Against "Cost-Free" Representations

Is visual perception “rich” or “sparse?” One finding supporting the “rich” hypothesis shows that a specific visual summary representation, color variability, is represented “cost-free” outside focally-attended regions in dual-task paradigms (Bronfman et al. 2014). Here, we investigated whether this “cost-free” phenomenon for color variability perception extends to peripheral vision. We performed three experiments: in our first experiment, we replicated previous findings and verified that color variability is represented “cost-free” in central vision. In our second experiment, we extended the paradigm to peripheral vision and found that in minimally-attended regions of space, color variability perception was impaired. In a third and final experiment, we added confidence judgments to our task, and found that participants maintained high levels of metacognitive awareness of impaired performance in minimally-attended regions of space. These findings provide evidence which challenges common conceptions on both sides of the rich vs. sparse debate.

State-based network similarity visualization

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.