Jason Tang

Jason Tang

Data Science Graduate student in National Taiwan University & Academia Sinica

Publications

Retrieving Implicit Information for Stock Movement Prediction

Published in ACM SIGIR, 2021

Previous studies on the financial news focus mainly on the news articles explicitly mentioning the target financial instruments, and may suffer from data sparsity. As taking into consideration other related news, e.g., sector-related news, is a crucial part of real-world decision-making, we explore the use of news without explicit target mentions to enrich the information for the prediction model. We develop a neural network framework that jointly learns with a news selection mechanism to extract implicit information from the chaotic daily news pool. Our proposed model, called the news distilling network (NDN), takes advantage of neural representation learning and collaborative filtering to capture the relationship between stocks and news. With NDN, we learn latent stock and news representations to facilitate similarity measurements, and apply a gating mechanism to prevent noisy news representations from flowing to a higher level encoding stage, which encodes the selected news representation of each day. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework and show improvements over previous techniques.

Recommended citation: Tsun-Hsien Tang, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. Retrieving Implicit Information for Stock Movement Prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021). https://dl.acm.org/doi/10.1145/3404835.3462999

FinSense: An Assistant System for Financial Journalists and Investors

Published in ACM WSDM, 2021

This paper demonstrates FinSense, a system that improves the working efficiency of financial information processing. Given the draft of a financial news story, FinSense extracts the explicit-mentioned stocks and further infers the implicit stocks, providing insightful information for decision making. We propose a novel graph convolutional network model that performs implicit financial instrument inference toward the in-domain data. In addition, FinSense generates candidate headlines for the draft, reducing a significant amount of time in journalism production. The proposed system also provides assistance to investors to sort out the information in the financial news articles.

Recommended citation: Yi-Ting Liou, Chung-Chi Chen, Tsun-Hsien Tang, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. FinSense: An Assistant System for Financial Journalists and Investors. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021). https://dl.acm.org/doi/10.1145/3437963.3441704

Truncated SVD-based Feature Engineering for Short Video Understanding and Recommendation

Published in IEEE ICME Grand Challenge, 2019

Short video app, like TikTok, has received wide acclaim due to the prevalence of social media and the availability of recording devices such as mobile phones. Moreover, with the advent of the big data age, the use of historical user behaviors from multi-modal resources plays a pivotal role in the video recommendation system. In the ICME 2019 Short Video Understanding Challenge, participants are asked to predict whether a user will finish and like a specific short video along with its multi-modal features, i.e., the problem is formulated as a click-through rate prediction task. In this paper, we present an ensemble of unconventional models to the task, including tailored neural networks structure based on Compressed Interaction Network (CIN) and Gradient Boosting Decision Trees (GDBTs) using classic SVD-based features. We achieved a weighted AUC score of 0.8029 and 0.8037 on the Public and Private Leaderboard of track2, respectively, and ended up with the 3$^{rd}$ place in the competition.

Recommended citation: Tsun-Hsien Tang, Kuan-Ta Chen and Hsin-Hsi Chen (2019). “Truncated SVD-based Feature Engineering for Short Video Understanding and Recommendation.” In Proceedings of IEEE International Conference on Multimedia and Expo 2019, Short Video Understanding Challenge, July 8-12, 2019, Shanghai, China. https://ieeexplore.ieee.org/document/8794898

Visual Concept Selection with Textual Knowledge for Understanding Activities of Daily Living and Life Moment Retrieval

Published in CLEF, 2018

This paper presents our approach to the task of ImageCLEFlifelog 2018. Two subtasks, activities of daily living understanding (ADLT) and life moment retrieval (LMRT) are addressed. We attempt to reduce the user involvement during the retrieval stage by using natural language processing technologies. The two subtasks are conducted with dedicated pipelines, while similar methodology is shared. We first obtain visual concepts from the images with a wide range of computer vision tools and propose a concept selection method to prune the noisy concepts with word embeddings in which textual knowledge is inherent. For ADLT, the retrieved images of a given topic are sorted by time, and the frequency and duration are further calculated. For LMRT, the retrieval is based on the ranking of similarity between image concepts and user queries. In terms of the performance, our systems achieve 47.87% of percentage dissimilarity in ADLT and 39.5% of F1@10 in LMRT.

Recommended citation: Tsun-Hsien Tang, Min-Huan Fu, Hen-Hsen Huang, Kuan-Ta Chen and Hsin-Hsi Chen (2018). “Visual Concept Selection with Textual Knowledge for Understanding Activities of Daily Living and Life Moment Retrieval.” In Working Notes of Conference and Labs of the Evaluation Forum (CLEF 2018), Avignon, France, 10-14 September 2018. http://ceur-ws.org/Vol-2125/paper_124.pdf