earticle

영어

Real-time interaction between anchors and viewers is a defining feature of live streaming e-commerce, shaping re-lational engagement and immediate purchase decisions. However, existing sales prediction models largely rely on aggregated behavioral metrics, overlooking the temporal and reciprocal dynamics that drive sales outcomes. Ana-lyzing 7,684 broadcasts, we find that sales performance depends on distinctive temporal trajectories of interaction rather than static engagement levels. To address this, we propose AVITSNet (Anchor–Viewer Interaction-aware Time Series Network), a multimodal deep learning framework that explicitly models the temporal and bidirectional flow of anchor–viewer interactions. Experimental results show that AVITSNet consistently outperforms conventional and hybrid baselines across all metrics. Textual signals (anchor speech and viewer comments) serve as key predictors, while be-havioral and contextual variables provide complementary value. Attention analysis further highlights the predictive importance of early viewer responses.