Product recommendation system based user purchase criteria and product reviews

In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured data, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions

Want-To vs. Have-To Socializations in Social Network Sites: Fear of Isolation, Jealousy, and Tie Strengths

As social network sites (SNS) expand the boundaries of one’s social life, we often observe encounters between two different types of motivations for socialization - I want-to socialize vs. I have-to socialize. SNS at present are considered commodities. People do not always start using SNS because they want to, but often because everyone else is using it; people do not wish to be isolated from social circles. This study aims to examine different types of user motivations in SNS and observe how these lead to actual socialization behaviours with different progress dynamics. We apply constraint- and dedication- based relationship framework to distinguish motivations and identify constructs for each motivation. We plan to collect data from one of the major SNS to validate how their socialization intentions are differently realized into actual behaviours. We develop a two-staged research model and this research-in-progress presents the result of the pilot study conducted for the first stage. We also discuss how the second stage of the study will be executed, and how it will benefit the related literature when the project is successfully completed

Learning to Discriminate Information for Online Action Detection

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an input image sequence includes background and irrelevant actions as well as the action of interest. For online action detection, in this paper, we propose a novel recurrent unit to explicitly discriminate the information relevant to an ongoing action from others. Our unit, named Information Discrimination Unit (IDU), decides whether to accumulate input information based on its relevance to the current action. This enables our recurrent network with IDU to learn a more discriminative representation for identifying ongoing actions. In experiments on two benchmark datasets, TVSeries and THUMOS-14, the proposed method outperforms state-of-the-art methods by a significant margin. Moreover, we demonstrate the effectiveness of our recurrent unit by conducting comprehensive ablation studies.Comment: To appear in CVPR 202

Deformable Graph Transformer

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically sampled relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves state-of-the-art performance on 7 graph benchmark datasets with 2.5 - 449 times less computational cost compared to transformer-based graph models with full attention.Comment: 16 pages, 3 figure

Malignant Pleural Effusion from Metastatic Prostate Cancer: A Case Report with Unusual Cytologic Findings

We present a case of 55-year-old man who complained of dyspnea and sputum for a month. He was an ex-smoker with a history of prostate cancer and pulmonary tuberculosis. Chest radiographs revealed bilateral pleural effusions of a small to moderate amount. Pigtail catheters were inserted for drainage. The pleural fluid consisted of large clusters and tightly cohesive groups of malignant cells, which however could not be ascribed to prostate cancer with certainty. We performed immunocytochemical panel studies to determine the origin of cancer metastasis. The immunostaining results were positive for prostate-specific antigen, alpha-methylacyl-coenzyme A racemase, and Nkx 3.1, consistent with prostate cancer. Pleural effusion associated with prostate cancer is rare. To our knowledge, this is the first case report in Korea to describe cytologic features of malignant pleural effusion associated with prostate cancer