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The recommendation has been playing a key role in many industries, e.g.,e-commerce, streaming media, social media, etc. Recently, a new recommendationscenario, called Trigger-Induced Recommendation (TIR), where users are able toexplicitly express their instant interests via trigger items, is emerging as anessential role in many e-commerce platforms, e.g., Alibaba.com and Amazon.Without explicitly modeling the user's instant interest, traditionalrecommendation methods usually obtain sub-optimal results in TIR. Even thoughthere are a few methods considering the trigger and target items simultaneouslyto solve this problem, they still haven't taken into account temporalinformation of user behaviors, the dynamic change of user instant interest whenthe user scrolls down and the interactions between the trigger and targetitems. To tackle these problems, we propose a novel method -- Deep EvolutionalInstant Interest Network (DEI2N), for click-through rate prediction in TIRscenarios. Specifically, we design a User Instant Interest Modeling Layer topredict the dynamic change of the intensity of instant interest when the userscrolls down. Temporal information is utilized in user behavior modeling.Moreover, an Interaction Layer is introduced to learn better interactionsbetween the trigger and target items. We evaluate our method on several offlineand real-world industrial datasets. Experimental results show that our proposedDEI2N outperforms state-of-the-art baselines. In addition, online A/B testingdemonstrates the superiority over the existing baseline in real-worldproduction environments.

Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation