NDLI: Cross-Domain Recommendation via Clustering on Multi-Layer Graphs

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Forward to the Past: Notes towards a Pre-history of Web Search

Mail Search: It's Getting Personal!

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Cross-Domain Recommendation via Clustering on Multi-Layer Graphs

Content Provider	ACM Digital Library
Author	Farseev, Aleksandr Samborskii, Ivan Chua, Tat-Seng Filchenkov, Andrey
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 $C^{3}R,$ which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new cross-source 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.
Starting Page	195
Ending Page	204
Page Count	10
File Format	PDF
ISBN	9781450350228
DOI	10.1145/3077136.3080774
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User community detection Grassmann manifolds Multi-source learning Cross-domain recommendation Data fusion Multi-source clustering Spectral clustering Cross-source recommendation Multi-layer clustering Multi-view learning Recommender systems
Content Type	Text
Resource Type	Article

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