Due to the explosive growth of social network services, friendship inference has been widely adopted by Online Social Service Providers (OSSPs) for friend recommendation. The conventional techniques, however, have limitations in accuracy or scalability to handle such a large yet sparse multi-source data. For example, the OSSPs will be required to manually give the order in which the various information is applied. This unavoidably reduces the applicability of existing friend recommendation systems. To address this issue, we propose a Two-stage Deep learning framework for Friendship Inference (TDFI). This approach can utilize multi-source information simultaneously with low complexity. In particular, we apply an Extended Adjacency Matrix (EAM) to represent the multi-source information. We then adopt an improved Deep AutoEncoder Network (iDAEN) to extract the fused feature vector for each user. The TDFI framework also provides an improved Deep Siamese Network (iDSN) to measure user similarity from iDAEN. Finally, we evaluate the effectiveness and robustness of TDFI on three large-scale real-world datasets. It shows that TDFI can effectively handle the sparse multi-source data while providing better accuracy for friend recommendation.
|Original language||English (US)|
|Title of host publication||INFOCOM 2019 - IEEE Conference on Computer Communications|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|State||Published - Apr 2019|
|Event||2019 IEEE Conference on Computer Communications, INFOCOM 2019 - Paris, France|
Duration: Apr 29 2019 → May 2 2019
|Name||Proceedings - IEEE INFOCOM|
|Conference||2019 IEEE Conference on Computer Communications, INFOCOM 2019|
|Period||4/29/19 → 5/2/19|
Bibliographical noteFunding Information:
This work in this paper was in part supported by the National Key R&D Program of China under Grant No. 2018YFB0803405, China National Funds for Distinguished Young Scientists under Grant No. 61825204 and Beijing Outstanding Young Scientist Project.
© 2019 IEEE.