Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Yicong Li
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
A Distributed Topic Model for Large-Scale Streaming Text
Published in International Conference on Knowledge Science, Engineering and Management, 2019
Learning topic information from large-scale unstructured text has attracted extensive attention from both the academia and industry. Topic models, such as LDA and its variants, are a popular machine learning technique to discover such latent structure. Among them, online variational hierarchical Dirichlet process (onlineHDP) is a promising candidate for dynamically processing streaming text. Instead of a static assignment in advance, the number of topics in onlineHDP is inferred from the corpus as the training process proceeds. However, when dealing with large scale streaming data it still suffers from the limited model capacity problem. To this end, we proposed a distributed version of the onlineHDP algorithm (named as DistHDP) in this paper, the training task is split into many sub-batch tasks and distributed across multiple worker nodes, such that the whole training process is accelerated. The model convergence is guaranteed through a distributed variation inference algorithm. Extensive experiments conducted on several real-world datasets demonstrate the usability and scalability of the proposed algorithm.
Recommended citation: Yicong, Li. (2019). "A Distributed Topic Model for Large-Scale Streaming Text." International Conference on Knowledge Science, Engineering and Management. https://link.springer.com/chapter/10.1007/978-3-030-29563-9_4
Temporal Meta-path Guided Explainable Recommendation
Published in WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendation only utilizes static knowledge graph and ignores the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling users’ temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling users’ sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation (TMER), which utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users’ historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent strong baselines.
Recommended citation: Yicong, Li. (2019). "Temporal Meta-path Guided Explainable Recommendation." International Conference on Web Search and Data Mining. https://arxiv.org/pdf/2101.01433.pdf
Hyperbolic hypergraphs for sequential recommendation
Published in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender systems. However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty. Moreover, existing sequential hypergraph recommendation overlooks the temporal modelling among user relationships, which neglects rich social signals from the recommendation data. To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) with the pre-training phase. Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings). In the recommendation phase, we learn multi-scale item embeddings via a hierarchical structure to capture multiple time-span information. To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings. Also, we design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness. Extensive experiments are conducted on two real-world datasets to prove the effectiveness and high performance of the model.
Recommended citation: Yicong, Li. (2021). "Hyperbolic hypergraphs for sequential recommendation." International Conference on Information \& Knowledge Management. https://dl.acm.org/doi/pdf/10.1145/3459637.3482351
Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation
Published in IEEE Transactions on Knowledge and Data Engineering, 2023
“Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information from knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although some works boost the performance and explainability of recommendations through modeling the user’s temporal sequential behavior, most of them either only focus on modeling the user’s sequential interactions within a path or independently and separately of the recommendation mechanism. Moreover, some path-based explainable recommendations use random selection or traditional machine learning methods to decrease the volume of explainable paths, which cannot guarantee high quality of the explainable paths for the recommendation. To deal with the problem, recent path exploration use reinforcement learning to improve diversity and quality. However, unsupervised training leads to low-efficiency path exploration. Therefore, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes supervised reinforcement learning to explore item-item paths between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on a dynamic knowledge graph for the explainable recommendation. Extensive evaluations of TMER-RL on two real-world datasets show state-of-the-art performance compared to recent strong baselines.”
Recommended citation: Yicong, Li. (2023). "Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation." IEEE Transactions on Knowledge and Data Engineering. https://arxiv.org/pdf/2111.12262
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
Recent studies successfully learned static graph embeddings that are structurally fair by preventing the effectiveness disparity of high- and low-degree vertex groups in downstream graph mining tasks. However, achieving structure fairness in dynamic graph embedding remains an open problem. Neglecting degree changes in dynamic graphs will significantly impair embedding effectiveness without notably improving structure fairness. This is because the embedding performance of high-degree and low-to-high-degree vertices will significantly drop close to the generally poorer embedding performance of most slightly changed vertices in the long-tail part of the power-law distribution. We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biased structural evolutions by jointly embedding the connection changes among vertices and the long-short-term evolutionary trend of vertex degrees. Furthermore, a novel dual debiasing approach is devised to encode fair embeddings contrastively, customizing debiasing strategies for different biased structural evolutions. This innovative debiasing strategy breaks the effectiveness bottleneck of embeddings without notable fairness loss. Extensive experiments demonstrate that FairDGE achieves simultaneous improvement in the effectiveness and fairness of embeddings.
Recommended citation: Yicong, Li. (2024). "Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach." ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://dl.acm.org/doi/10.1145/3637528.3671848
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
Published in IEEE Transactions on Knowledge and Data Engineering, 2024
“Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method.”
Recommended citation: Yicong, Li. (2024). "Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation." IEEE Transactions on Knowledge and Data Engineering. https://arxiv.org/pdf/2401.05744