In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, p. 2184-2190
Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The...
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In: The Web Conference 2021, Ljubljana, Slovenia, April 12-23, 2021, 2021, p. 1-12
Knowledge Graph (KG) completion has been widely studied to tackle the incompleteness issue (i.e., missing facts) in modern KGs. A fact in a KG is represented as a triplet (ℎ, 𝑟, 𝑡 ) linking two entities ℎ and 𝑡 via a relation 𝑟 . Existing work mostly consider link prediction to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (ℎ,...
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In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p. 1162–1172
Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques...
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In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, p. 437–446
The graph embedding paradigm projects nodes of a graph into a vector space, which can facilitate various downstream graph analysis tasks such as node classification and clustering. To efficiently learn node embeddings from a graph, graph embedding techniques usually preserve the proximity between node pairs sampled from the graph using random walks. In the context of a heterogeneous graph,...
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In: IEEE Transactions on Knowledge and Data Engineering, 2019, vol. 31, no. 3, p. 507–520
Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from...
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In: Proceedings of the Web Conference (WWW 2019)
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In: IEEE Transactions on Knowledge and Data Engineering, 2018, p. 1–1
Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming histograms, where the elements of a histogram are observed one after the other in an online manner. The ever-growing cardinality of histogram elements over the data streams makes any similarity computation inefficient in that case. To...
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In: Journal of Network and Computer Applications, 2018, vol. 121, p. 59–69
The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of...
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In: Encyclopedia of Big Data Technologies, 2018, p. 1–7
With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al. 2008) or Google’s Knowledge Graph (Google 2014), entities are connected via relations. For example, Bern is capital...
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In: 2017 IEEE International Conference on Data Mining (ICDM), 2017, p. 545–554
Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming data, where the elements of a histogram are observed in a streaming manner. First, the ever-growing cardinality of histogram elements makes any similarity computation inefficient. Second, the concept-drift issue in the data streams also...
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