36 citations · 63 across the 11 of their papers we have counts for
11 papers
Improving Conversational Passage Re-ranking with View Ensemble
Jia-Huei Ju, Sheng-Chieh Lin, Ming-Feng Tsai +1
This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality o…
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations
Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen +4
Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of…
Personalized TV Recommendation: Fusing User Behavior and Preferences
Sheng-Chieh Lin, Ting-Wei Lin, Jing-Kai Lou +2
In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channe…
Skewness Ranking Optimization for Personalized Recommendation
Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen +1
In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifi…
Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting
Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3
Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retriev…
Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models
Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3
This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLM…
TTTTTackling WinoGrande Schemas
Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira +3
We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using…
Collaborative Similarity Embedding for Recommender Systems
Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai +1
We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representa…
Representation Learning for Image-based Music Recommendation
Chih-Chun Hsia, Kwei-Herng Lai, Yian Chen +2
Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for con…
Superhighway: Bypass Data Sparsity in Cross-Domain CF
Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi +3
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods foc…
Vertex-Context Sampling for Weighted Network Embedding
Chih-Ming Chen, Yi-Hsuan Yang, Yian Chen +1
In recent years, network embedding methods have garnered increasing attention because of their effectiveness in various information retrieval tasks. The goal is to learn low-dimens…