Estimating Click-Through Rate (CTR) is a vital yet challenging task in
personalized product search. However, existing CTR methods still struggle in
the product search settings due to the following three challenges including how
to more effectively extract users' short-term interests with respect to
multiple aspects, how to extract and fuse users' long-term interest with
short-term interests, how to address the entangling characteristic of long and
short-term interests. To resolve these challenges, in this paper, we propose a
new approach named Hierarchical Interests Fusing Network (HIFN), which consists
of four basic modules namely Short-term Interests Extractor (SIE), Long-term
Interests Extractor (LIE), Interests Fusion Module (IFM) and Interests
Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's
short-term interests by integrating three fundamental interests encoders within
it namely query-dependent, target-dependent and causal-dependent interest
encoder, respectively, followed by delivering the resultant representation to
the module LIE, where it can effectively capture user long-term interests by
devising an attention mechanism with respect to the short-term interests from
SIE module. In IFM, the achieved long and short-term interests are further
fused in an adaptive manner, followed by concatenating it with original raw
context features for the final prediction result. Last but not least,
considering the entangling characteristic of long and short-term interests, IDM
further devises a self-supervised framework to disentangle long and short-term
interests. Extensive offline and online evaluations on a real-world e-commerce
platform demonstrate the superiority of HIFN over state-of-the-art methods.

本文提出一种名为 HIFN 的层次兴趣融合网络，该网络通过包含短期兴趣提取器、长期兴趣提取器、兴趣融合模块和兴趣解缠模块这四个基本模块，有效地提取和融合用户的短期和长期兴趣，以提高个性化产品搜索中的点击率预测效果。

层级融合长短期用户兴趣用于产品搜索中点击率预测

Hierarchically Fusing Long and Short-Term User Interests for  Click-Through Rate Prediction in Product Search

Sequential recommender systems aim to predict users' next interested item
given their historical interactions. However, a long-standing issue is how to
distinguish between users' long/short-term interests, which may be
heterogeneous and contribute differently to the next recommendation. Existing
approaches usually set pre-defined short-term interest length by exhaustive
search or empirical experience, which is either highly inefficient or yields
subpar results. The recent advanced transformer-based models can achieve
state-of-the-art performances despite the aforementioned issue, but they have a
quadratic computational complexity to the length of the input sequence. To this
end, this paper proposes a novel sequential recommender system, AutoMLP, aiming
for better modeling users' long/short-term interests from their historical
interactions. In addition, we design an automated and adaptive search algorithm
for preferable short-term interest length via end-to-end optimization. Through
extensive experiments, we show that AutoMLP has competitive performance against
state-of-the-art methods, while maintaining linear computational complexity.

提出了一种新型的、自适应的持续推荐系统 AutoMLP，旨在更好地对用户的长期和短期兴趣进行建模，通过端到端优化进行自动化和自适应搜索算法，与最先进的方法具有相当竞争性的性能，同时保持线性计算复杂度。

AutoMLP：面向序列推荐的自动 MLP

AutoMLP: Automated MLP for Sequential Recommendations

Modeling user's long-term and short-term interests is crucial for accurate
recommendation. However, since there is no manually annotated label for user
interests, existing approaches always follow the paradigm of entangling these
two aspects, which may lead to inferior recommendation accuracy and
interpretability. In this paper, to address it, we propose a Contrastive
learning framework to disentangle Long and Short-term interests for
Recommendation (CLSR) with self-supervision. Specifically, we first propose two
separate encoders to independently capture user interests of different time
scales. We then extract long-term and short-term interests proxies from the
interaction sequences, which serve as pseudo labels for user interests. Then
pairwise contrastive tasks are designed to supervise the similarity between
interest representations and their corresponding interest proxies. Finally,
since the importance of long-term and short-term interests is dynamically
changing, we propose to adaptively aggregate them through an attention-based
network for prediction. We conduct experiments on two large-scale real-world
datasets for e-commerce and short-video recommendation. Empirical results show
that our CLSR consistently outperforms all state-of-the-art models with
significant improvements: GAUC is improved by over 0.01, and NDCG is improved
by over 4%. Further counterfactual evaluations demonstrate that stronger
disentanglement of long and short-term interests is successfully achieved by
CLSR. The code and data are available at
this https URL

通过对长期和短期兴趣进行对比学习，提出了一种自我监督的框架来解开两个方面之间的纠缠，为推荐系统提供更准确、更有解释性的建议。在电商和短视频数据集上的实验结果表明，该方法优于目前所有的最新模型，并且能够成功地实现长期和短期兴趣的更强的解脱。