LiangShangsong

Bio: LiangShangsong is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Profiling (information science) & Question answering. The author has co-authored 1 publications.

Profiling Users for Question Answering Communities via Flow-Based Constrained Co-Embedding Model

Abstract: In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as ato...

Drinking Chai with Your (AI) Programming Partner: A Design Fiction about Generative AI for Software Engineering 107-122

Abstract: Using design fiction, we develop a series of possible generative AI features and applications that could be developed in the future of humans’ roles in software engineering. We use the fiction to highlight choices and value-tensions among these potential futures.

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

Abstract: Knowledge graph embedding (KGE) is a increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this paper, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.

Topic-Guided Self-Introduction Generation for Social Media Users

Abstract: Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.

Enhancing Conversational Recommendation Systems with Representation Fusion

Abstract: Conversational Recommendation Systems (CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS first constructs questions and then asks users for their feedback in each conversation session to refine better recommendation lists to users. The key design of CRS is to construct proper questions and obtain users’ feedback in response to these questions so as to effectively capture user preferences. Many CRS works have been proposed; however, they suffer from defects when constructing questions for users to answer: (1) employing a dialogue policy agent for constructing questions is one of the most common choices in CRS, but it needs to be trained with a huge corpus, and (2) it is not appropriate that constructing questions from a single policy (e.g., a CRS only selects attributes that the user has interacted with) for all users with different preferences. To address these defects, we propose a novel CRS model, namely a Representation Fusion–based Conversational Recommendation model, where the whole conversation session is divided into two subsessions (i.e., Local Question Search subsession and Global Question Search subsession) and two different question search methods are proposed to construct questions in the corresponding subsessions without employing policy agents. In particular, in the Local Question Search subsession we adopt a novel graph mining method to find questions, where the paths in the graph between users and attributes can eliminate irrelevant attributes; in the Global Question Search subsession we propose to initialize user preference on items with the user and all item historical rating records and construct questions based on user’s preference. Then, we update the embeddings independently over the two subsessions according to user’s feedback and fuse the final embeddings from the two subsessions for the recommendation. Experiments on three real-world recommendation datasets demonstrate that our proposed method outperforms five state-of-the-art baselines.

Examining User Heterogeneity in Digital Experiments

Abstract: Digital experiments are routinely used to test the value of a treatment relative to a status quo control setting — for instance, a new search relevance algorithm for a website or a new results layout for a mobile app. As digital experiments have become increasingly pervasive in organizations and a wide variety of research areas, their growth has prompted a new set of challenges for experimentation platforms. One challenge is that experiments often focus on the average treatment effect (ATE) without explicitly considering differences across major sub-groups — heterogeneous treatment effect (HTE). This is especially problematic because ATEs have decreased in many organizations as the more obvious benefits have already been realized. However, questions abound regarding the pervasiveness of user HTEs and how best to detect them. We propose a framework for detecting and analyzing user HTEs in digital experiments. Our framework combines an array of user characteristics with double machine learning. Analysis of 27 real-world experiments spanning 1.76 billion sessions and simulated data demonstrates the effectiveness of our detection method relative to existing techniques. We also find that transaction, demographic, engagement, satisfaction, and lifecycle characteristics exhibit statistically significant HTEs in 10% to 20% of our real-world experiments, underscoring the importance of considering user heterogeneity when analyzing experiment results, otherwise personalized features and experiences cannot happen, thus reducing effectiveness. In terms of the number of experiments and user sessions, we are not aware of any study that has examined user HTEs at this scale. Our findings have important implications for information retrieval, user modeling, platforms, and digital experience contexts, in which online experiments are often used to evaluate the effectiveness of design artifacts.