Name
Abstract | ||
|---|---|---|
ABSTRACTSocial Resonance is a common socio-behavioral phenomenon in which users are more influenced by opinions that have similar vibes. That is, opinions from two different groups of users can mutually reinforce (or resonate with) each other to have an even stronger impact on the user. In this paper, we explore the powerful social resonance effect between social connections and other users in an eCommerce platform to improve recommendation. Specifically, we first formulate an item-aware user influence network that connects users who rate the same item. With the social network and item-aware user influence network, a novel graph-based mutual learning framework is proposed, which captures the resonance influence from both user local correlations and global connections. We then fuse these influence paths to predict the resonance-enhanced user preference towards items. Experiments on public benchmarks show the proposed approach outperforms state-of-the-art social recommendation methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3487351.3488335 | Knowledge Discovery and Data Mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Zhang | 1 | 3492 | 281.04 |
| Yun He | 2 | 15 | 6.64 |
| James Caverlee | 3 | 2484 | 145.47 |