###### Abstract  
Modelling both long- and short-term user interests from historical data is crucial for generating accurate recommendations. However, unifying these metrics across multiple application domains can be challenging, and existing approaches often rely on complex, intertwined models which can be difficult to interpret. To address this issue, we propose a lightweight, plug-and-play interest enhancement module that fuses interest vectors from two independent models. After analyzing the dataset, we identify deviations in the recommendation performance of long- and short-term interest models. To compensate for these differences, we use feature enhancement and loss correction during training. In the fusion process, we explicitly split long-term interest features with longer duration into multiple local features. We then use a shared attention mechanism to fuse multiple local features with short-term interest features to obtain interaction features. To correct for bias between models, we introduce a comparison learning task that monitors the similarity between local features, short-term features, and interaction features. This adaptively reduces the distance between similar features. Our proposed module combines and compares multiple independent long-term and short-term interest models on multiple domain datasets. As a result, it not only accelerates the convergence of the models but also achieves outstanding performance in challenging recommendation scenarios. recommendation system; contrast learning; deep learning +
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the length of the sequence data and combine them to make recommendations that balance between personalization and diversity. **Long-term interests:** extracting stable interests from sequential data based on long-term interests has always been a research focus. A common solution is to learn longer user behavior sequences as much as possible and store user interest features offline. DIN [2] considers that each user's attention to the target item should be different and proposes a model based on attention mechanism, which uses target objects and historical sequences to calculate attention scores to update sequence information. MIMN [15] decouples user interest memory storage units from recommendation modules. Long-term user interests do not need to change in a short time, and offline storage can remember longer user sequence data. SIM [16] proposes a method for quickly retrieving user behavior memory sequences throughout the user's life cycle, and improves the memory sequence to tens of thousands. SURGE [17] integrates different types of preferences in long-term user behaviors into clusters in the graph. Although longer sequence data can extract more stable user interests, there are also problems with difficult updates, difficult training convergence, and high data requirements. **Short-term interests:** deep learning models based on time-series analysis have been found to be effective for modeling short-term user interests [3; 12; 14; 18; 19]. Several notable models, including GRU4 REC [14] and DIEN [3], have integrated recurrent neural networks and attention mechanisms to improve recommendation accuracy. More recent models such as SDM [12] and CGNN-MHSA-AR [20] have further enhanced these approaches by incorporating multi-dimensional information. However, short-term interest models have limitations in dealing with noisy and incomplete data, which can lead to bias in recommendation results when coordinated with long-term interest models. **LS-term interests:** typically, recommendation systems rely on either long-term or short-term benefits to generate recommendations. However, considering only one side interest will lose the platform and user experience [21]. Recent methods [1; 12; 18; 21; 22; 23; 24] have proposed solutions to this problem by dividing the model into two parts that separately model the user's long-term and short-term interests. The final recommendation is then based on a fusion of these two models.