ACM Transactions on Knowledge Discovery from Data

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, ...

The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based ...

The unprecedented amounts of data have catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently. One crucial challenge in ...

The presence of data noise and corruptions has recently invoked increasing attention on robust least-squares regression (RLSR), which addresses this fundamental problem that learns reliable regression coefficients when response variables can be ...

In this article, we aim at developing a user representation learning model to solve the information diffusion prediction problem in social media. The main idea is to project the diffusion users into a continuous latent space as the role-based (sender ...

Restricted Boltzmann machine (RBM) is a famous model for feature extraction and can be used as an initializer for neural networks. When applying the classic RBM to multidimensional data such as 2D/3D tensors, one needs to vectorize such as high-order ...

The ever-increasing number of smartphone applications (apps) available on different app markets poses a challenge for personalized app recommendation. Conventional collaborative filtering-based recommendation methods suffer from sparse and binary user-...

Expert finding is the task of retrieving and ranking knowledgeable people in the subject of user’s query. It is a well-studied problem that has attracted the attention of many researchers. The most important challenge in expert finding is to determine ...

Sentiment classification is a popular text mining task in which textual content (e.g., a message) is assigned a polarity label (typically positive or negative) reflecting the sentiment expressed in it. Sentiment classification is used widely in ...

Complements and substitutes are two typical product relationships that deserve consideration in online product recommendation. One of the key objectives of recommender systems is to promote cross-selling, which heavily relies on recommending the ...

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. ...