2,549 research outputs found
Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation
Next point-of-interest (POI) recommendation is a critical task in
location-based social networks, yet remains challenging due to a high degree of
variation and personalization exhibited in user movements. In this work, we
explore the latent hierarchical structure composed of multi-granularity
short-term structural patterns in user check-in sequences. We propose a
Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next
POI recommendation, which employs stacked hierarchical encoders to recursively
encode the spatio-temporal context and explicitly locate subsequences of
different granularities. More specifically, in each encoder, the global
attention layer captures the spatio-temporal context of the sequence, while the
local attention layer performed within each subsequence enhances subsequence
modeling using the local context. The sequence partition layer infers positions
and lengths of subsequences from the global context adaptively, such that
semantics in subsequences can be well preserved. Finally, the subsequence
aggregation layer fuses representations within each subsequence to form the
corresponding subsequence representation, thereby generating a new sequence of
higher-level granularity. The stacking of encoders captures the latent
hierarchical structure of the check-in sequence, which is used to predict the
next visiting POI. Extensive experiments on three public datasets demonstrate
that the proposed model achieves superior performance whilst providing
explanations for recommendations. Codes are available at
https://github.com/JennyXieJiayi/STAR-HiT
An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research
The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed
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