Cross-Domain Recommendation via Clustering on Multi-Layer Graphs
Aleksandr Farseev Ivan Samborskii Andrey Filchenkov Tat-Seng Chua
Aleksandr Farseev
Ivan Samborskii
Andrey Filchenkov
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Abstract
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 C3R, 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. © 2017 Copyright held by the owner/author(s).
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Source Title
ACM SIGIR 2017
Publisher
Association for Computing Machinery, Inc
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Date
2017-08-07
DOI
10.1145/3077136.3080774
Type
Conference Paper