Xiao-li Li

IJCAI Conference 2018 Conference Paper

Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback

• Yong Liu
• Lifan Zhao
• Guimei Liu
• Xinyan Lu
• Peng Gao
• Xiao-li Li
• Zhihui Jin

Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods.

IJCAI Conference 2018 Conference Paper

Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns

• Ali Zonoozi
• Jung-jae Kim
• Xiao-li Li
• Gao Cong

Time-series forecasting in geo-spatial domains has important applications, including urban planning, traffic management and behavioral analysis. We observed recurring periodic patterns in some spatio-temporal data, which were not considered explicitly by previous non-linear works. To address this lack, we propose novel `Periodic-CRN' (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction. We show that PCRN consistently outperforms the state-of-the-art methods for crowd density prediction across two taxi datasets from Beijing and Singapore.

AAAI Conference 2017 Conference Paper

Incorporating Expert Knowledge into Keyphrase Extraction

• Sujatha Das Gollapalli
• Xiao-li Li
• Peng Yang

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words. In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surfaceform, and document-structure information through sequence labeling. We experimentally illustrate that using withindocument features alone, our tagger trained with Conditional Random Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.

IJCAI Conference 2017 Conference Paper

Learning User Dependencies for Recommendation

• Yong Liu
• Peilin Zhao
• Xin Liu
• Min Wu
• Lixin Duan
• Xiao-li Li

Social recommender systems exploit users' social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed based on pre-defined user dependencies. Thus, they may not be optimal for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy. In PRMF, users' latent features are assumed to follow a matrix variate normal (MVN) distribution. Both positive and negative user dependencies can be modeled by the row precision matrix of the MVN distribution. Moreover, we also propose an alternating optimization algorithm to solve the optimization problem of PRMF. Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model.

AIJ Journal 2017 Journal Article

Low-rank decomposition meets kernel learning: A generalized Nyström method

• Liang Lan
• Kai Zhang
• Hancheng Ge
• Wei Cheng
• Jun Liu
• Andreas Rauber
• Xiao-li Li
• Jun Wang

Low-rank matrix decomposition and kernel learning are two useful techniques in building advanced learning systems. Low-rank decomposition can greatly reduce the computational cost of manipulating large kernel matrices. However, existing approaches are mostly unsupervised and do not incorporate side information such as class labels, making the decomposition less effective for a specific learning task. On the other hand, kernel learning techniques aim at constructing kernel matrices whose structure is well aligned with the learning target, which improves the generalization performance of kernel methods. However, most kernel learning approaches are computationally very expensive. To obtain the advantages of both techniques and address their limitations, in this paper we propose a novel kernel low-rank decomposition formulation called the generalized Nyström method. Our approach inherits the linear time and space complexity via matrix decomposition, while at the same time fully exploits (partial) label information in computing task-dependent decomposition. In addition, the resultant low-rank factors can generalize to arbitrary new samples, rendering great flexibility in inductive learning scenarios. We further extend the algorithm to a multiple kernel learning setup. The experimental results on semi-supervised classification demonstrate the usefulness of the proposed method.

IJCAI Conference 2017 Conference Paper

Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization

• Yangtao Wang
• Lihui Chen
• Xiao-li Li

Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multi-view data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data object can belong to several clusters with different memberships is widely used in many applications. However, in most of the fuzzy clustering approaches, a single center or medoid is considered as the representative of each cluster in the end of clustering process. This may not be sufficient to ensure accurate data analysis. In this paper, a new multi-view fuzzy clustering approach based on multiple medoids and minimax optimization called M4-FC for relational data is proposed. In M4-FC, every object is considered as a medoid candidate with a weight. The higher the weight is, the more likely the object is chosen as the final medoid. In the end of clustering process, there may be more than one mediod in each cluster. Moreover, minimax optimization is applied to find consensus clustering results of different views with its set of features. Extensive experimental studies on several multi-view data sets including real world image and document data sets demonstrate that M4-FC not only outperforms single medoid based multi-view fuzzy clustering approach, but also performs better than existing multi-view relational clustering approaches.

AAAI Conference 2017 Conference Paper

What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM

• Linmei Hu
• Juanzi Li
• Liqiang Nie
• Xiao-li Li
• Chao Shao

Events are typically composed of a sequence of subevents. Predicting a future subevent of an event is of great importance for many real-world applications. Most previous work on event prediction relied on hand-crafted features and can only predict events that already exist in the training data. In this paper, we develop an end-to-end model which directly takes the texts describing previous subevents as input and automatically generates a short text describing a possible future subevent. Our model captures the two-level sequential structure of a subevent sequence, namely, the word sequence for each subevent and the temporal order of subevents. In addition, our model incorporates the topics of the past subevents to make context-aware prediction of future subevents. Extensive experiments on a real-world dataset demonstrate the superiority of our model over several state-of-the-art methods.

IJCAI Conference 2007 Conference Paper

• Xiao-li Li
• Bing Liu
• See-Kiong Ng

Traditional classification involves building a clas-sifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instances into the same set of classes. In practice, this paradigm can be problematic be-cause the test data may contain instances that do not belong to any of the previously defined classes. Detecting such unexpected instances in the test set is an important issue in practice. The problem can be formulated as learning from positive and unla-beled examples (PU learning). However, current PU learning algorithms require a large proportion of negative instances in the unlabeled set to be effec-tive. This paper proposes a novel technique to solve this problem in the text classification domain. The technique first generates a single artificial negative document AN. The sets P and {AN} are then used to build a naï ve Bayesian classifier. Our experiment results show that this method is significantly better than existing techniques.