Search Results

Search is probably the most common activity that humans conduct all the time. A search target can be a concrete item (with a yes or no answer and location information), an abstract concept (such as the most important information on the Web about Xindong ...

Learning partial Bayesian network (BN) structure is an interesting and challenging problem. In this challenge, it is computationally expensive to use global BN structure learning algorithms, while only one part of a BN structure is interesting, local BN ...

Feature selection is one of the core concepts in machine learning, which hugely impacts the model’s performance. For some real-world applications, features may exist in a stream mode that arrives one by one over time, while we cannot know the exact number ...

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid mining too many useless patterns. However, this method is difficult for users to set a suitable gap without prior knowledge and each character is considered to have the ...

Adverse drug-drug interaction (ADDI) is referred to as a situation where the unpleasant or adverse effects caused by the co-administration of two drugs, which becomes a significant problem for public health. With the increasing availability of healthcare ...

In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful ...

• Two different autoencoders are used to capture characteristics for users and items.
• Manifold regularization is integrated into autoencoder for user’s features learning.
• The comprehensive experiments evaluate the effectiveness of ...

In various recommender systems, ratings and reviews are the main information to show user preferences. However, recommendation models that only use ratings, such as collaborative filtering, are vulnerable to data sparsity. And models only using ...

• Mining information in reviews and interaction data from shared and specific views.
• Utilizing the confusion adversarial loss to extract the shared features.
• Enforcing orthogonal constraints to extract the specific features.
• ...

• Address self-adaptive HAOP mining which can discover extremely important patterns.

One-off sequential pattern mining (SPM) (or SPM under the one-off condition) is a kind of repetitive SPM with gap constraints, and has been widely applied in many fields. However, current research on one-off SPM ignores the utility (...

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, ...

Named entity recognition (NER) is a basic task of natural language processing (NLP), whose purpose is to identify named entities such as the names of persons, places, and organizations in the corpus. Utilizing neural networks for feature ...

Knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph ...

Many real-world applications have to face the problem of diversity in data formats and semantics. Currently, how to deal with heterogeneous data effectively is still a big challenge. With the rise of knowledge graphs, more and more applications are ...

This paper presents a HAO-Interaction question answering system, which exploits knowledge based question answering (KBQA) technology to quickly obtain an answer path for the input question, and then a creative text generation mechanism to acquire the ...

Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic ...

Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-...

For the multi-label text classification problems with many classes, many existing multi-label classification algorithms become infeasible or suffer an unaffordable cost. Some researches hence perform the Label Space Dimension Reduction(...

Nonoverlapping sequential pattern mining (SPM) is a data analysis task, which aims at identifying repetitive sequential patterns with gap constraint in a set of discrete sequences. Nonoverlapping means that any character in the ...

Linking ambiguous entity mentions in a text with their true mapping entities in a heterogeneous information network (HIN) is important. Most of existing entity linking methods with HINs assume that the entities in a text are ...

In a distribution channel, product manufacturers will reward retail traders who achieve a high value of sales. In order to obtain reward from the manufacturer, distributors might form an alliance where a cheating retail trader could accumulate ...

• Detecting sale accumulation behavior from multi-dimensional sparse data.
• Proposing a new concept of partially ordered lattice of the data warehouse schema.
• Proposing sale accumulation mining algorithms and sale accumulation pattern ...

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models ...