Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN.
|Pages||28 - 35|
|Publication status||Published - 2015|
|Event||International Conference on Advanced Cloud and Big Data - YangZhou, China|
Duration: 30 Oct 2015 → 1 Nov 2015
|Conference||International Conference on Advanced Cloud and Big Data|
|Abbreviated title||CBD 2015|
|Period||30/10/15 → 1/11/15|
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- online social networks
- topic community mining
- friend recommendation
- nonnegative matrix factorization
- multiplicative update
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