Science / Press

A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua. Cross-Domain Recommendation via Clustering on Multi-Layer Graphs 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17), August 7-11, 2017. 17 January 2019

Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users’ current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C 3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new crosssource user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge

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An algorithm that predicts relationship status with 86 per cent accuracy has decided President Trump is single 16 January 2019

President Donald Trump’s erratic Twitter habits led a sophisticated computer algorithm to conclude that he lives “like a bachelor”.

The artificial intelligence system, which is used to predict the marital status of social media accounts and claims to have an 86 per cent accuracy rate, identified Mr Trump as “not married”.

AI pegged Trump as a single guy based on his Twitter habits 16 January 2019

President Trump’s Twitter habits fooled Russian scientists into thinking he was a single guy, according to a new study.

Researchers from Information Technology, Optical Design and Engineering University in Saint Petersburg used artificial intelligence trained to determine a person’s marital status based on their social media use on tweets by Trump and former President Obama.

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