Learn More
Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we(More)
Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit(More)
Kineograph is a distributed system that takes a stream of incoming data to construct a continuously changing graph, which captures the relationships that exist in the data feed. As a computing platform, Kineograph further supports graph-mining algorithms to extract timely insights from the fast-changing graph structure. To accommodate graph-mining(More)
Temporal graphs capture changes in graphs over time and are becoming a subject that attracts increasing interest from the research communities, for example, to understand temporal characteristics of social interactions on a time-evolving social graph. Chronos is a storage and execution engine designed and optimized specifically for running in-memory(More)
pages 151–160, Dublin, Ireland, August 23-29 2014. A Probabilistic Model for Learning Multi-Prototype Word Embeddings Fei Tian†, Hanjun Dai∗, Jiang Bian‡, Bin Gao‡, Rui Zhang?, Enhong Chen†, Tie-Yan Liu‡ †University of Science and Technology of China, Hefei, P.R.China ∗Fudan University, Shanghai, P.R.China ‡Microsoft Research, Building 2, No. 5 Danling(More)
Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs k-means(More)
The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web(More)
Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing(More)
Understanding users'search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries without considering their context information. However, as exemplified by the well-known example on query(More)