arXiv subjects

Shui Liu

Publications and source records attributed to Shui Liu.

3 recordsLinked to original sources

arXiv2025

NAM: A Normalization Attention Model for Personalized Product Search In Fliggy

Personalized product search provides significant benefits to e-commerce platforms by extracting more accurate user preferences from historical behaviors. Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestimate those for long-tail items, resulting in inaccurate item similarities; Second, user purchasing propensity is highly heterogeneous according to the popularity of the target item: it is less correlated with the user's historical behavior for a popular item and more correlated for a long-tail item. To address these challenges, in this paper we propose NAM, a Normalization Attention Model, which optimizes ''when to personalize'' by utilizing Inverse Item Frequency (IIF) and employing a gating mechanism, as well as optimizes ''how to personalize'' by normalizing the attention mechanism from a global perspective. Through comprehensive experiments, we demonstrate that our proposed NAM model significantly outperforms state-of-the-art baseline models. Furthermore, we conducted an online A/B test at Fliggy, and obtained a significant improvement of 0.8% over the latest production system in conversion rate.

cs.IR↗

arXiv2025

MERIT: A Merchant Incentive Ranking Model for Hotel Search & Ranking

Online Travel Platforms (OTPs) have been working on improving their hotel Search & Ranking (S&R) systems that facilitate efficient matching between consumers and hotels. Existing OTPs focus almost exclusively on improving platform revenue. In this work, we take a first step in incorporating hotel merchants' objectives into the design of hotel S&R systems to achieve an incentive loop: the OTP tilts impressions and better-ranked positions to merchants with high quality, and in return, the merchants provide better service to consumers. Three critical design challenges need to be resolved to achieve this incentive loop: Matthew Effect in the consumer feedback-loop, unclear relation between hotel quality and performance, and conflicts between short-term and long-term revenue. To address these challenges, we propose MERIT, a MERchant IncenTive ranking model, which can simultaneously take the interests of merchants and consumers into account. We define a new Merchant Competitiveness Index (MCI) to represent hotel merchant quality and propose a new Merchant Tower to model the relation between MCI and ranking scores. Also, we design a monotonic structure for Merchant Tower to provide a clear relation between hotel quality and performance. Finally, we propose a Multi-objective Stratified Pairwise Loss, which can mitigate the conflicts between OTP's short-term and long-term revenue. The offline experiment results indicate that MERIT outperforms these methods in optimizing the demands of consumers and merchants. Furthermore, we conduct an online A/B test and obtain an improvement of 3.02% for the MCI score.

cs.IR↗

arXiv2025

TSRec: Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective

Repeat consumption, such as repurchasing items and relistening songs, is a common scenario in daily life. To model repeat consumption, the repeat-aware recommendation has been proposed to predict which item will be re-interacted based on the user-item interactions. In this paper, we investigate various inherent characteristics to enhance the repeat-aware recommendation. Specifically, we explore these characteristics from two aspects: one is from the temporal aspect where we consider the time interval relationship in the user behavior sequence; the other is from the sequential aspect where we consider the sequential-level relationship in the user behavior sequence. And our intuition is that both the temporal pattern and sequential pattern will reflect users' intentions of repeat consumption. By utilizing these two patterns, a novel model called Temporal and Sequential repeat-aware Recommendation(TSRec for short) is proposed to enhance repeat-aware recommendation. TSRec has three main components: 1) User-specific Temporal Representation Module (UTRM), which encodes and extracts user historical repeat temporal information. 2)Item-specific Temporal Representation Module (ITRM), which incorporates item time interval information as side information to alleviate the data sparsity problem of user repeat behavior sequence. 3) Sequential Repeat-Aware Module (SRAM), which represents the similarity between the user's current and the last repeat sequences. Extensive experimental results on three public benchmarks demonstrate the superiority of TSRec over state-of-the-art methods. The implementation code is available https://anonymous.4open.science/r/TSRec-2306/.

cs.IR↗