Deep click interest network for reranking hotels

Nowadays, e-commerce platforms of hotels have become a new trend to help people book hotels online. Interest modeling aims to automatically construct user interests that are critical for e-commerce platforms. Although interest modeling has achieved noticeable successes in product recommendations, it has not been explored in hotel search, especially in the reranking stage. This paper studies the interest modeling task for reranking hotels, and identifies two key challenges—high behavior sparsity and large interest gap. To address the challenges, we propose the deep click interest network (DCIN). High behavior sparsity keeps us from extracting rich semantics to characterize user interest preference. Accordingly, we propose the deep click concept to model the multi-view semantics of a click and then enrich the semantics of both a single click and a click sequence, so that the difficulty caused by the behavior sparsity can be addressed. To address the interest gap challenge, DCIN models user interests with two cascaded units: (i) Mutual-attention Interest Calibration Unit (ICU), which uses the candidate hotel to calibrate the embedding of every click, since the candidate hotel is selected by the ranking stage and can represent user current interests to some extent. So the gap between the calibrated embedding and user current interests can be diminished; (ii) Multi-attention Interest Aggregation Unit (IAU), which estimates the weight of a calibrated embedding from multiple perspectives, i.e., context, user feedback and interest consistency. So the calibrated embeddings, which are relevant to the current query context and important to the user, dominate user current interests, and thus the interest gap can be further diminished. Offline experiments over two large datasets and online A/B testing over the platform of Meituan-Hotel show that DCIN significantly outperforms the baselines and improved baselines. Notably, DCIN has been deployed in Meituan-Hotel, resulting in an increase of 2.40% in Click Through Conversion Rate (CTCVR) and that of 1.09% in Click Through Rate (CTR). •We study interest modeling for reranking hotels and identify two key challenges.•Deep click concept is formally defined to model the multi-view semantics of a click.•Mutual attention calibration unit is designed to calibrate the embedding of a click.•Multi-attention aggregation unit is developed to estimate the weights of clicks from multiple perspectives.•Both offline and online experiment