{"title":"用于增量推荐的潜在侧信息动态增强技术","authors":"Jing Zhang, Jin Shi, Jingsheng Duan, Yonggong Ren","doi":"10.1007/s10115-024-02165-9","DOIUrl":null,"url":null,"abstract":"<p>The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model’s sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step <i>T</i>, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step <span>\\(T+1\\)</span>, in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model’s efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"245 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Latent side-information dynamic augmentation for incremental recommendation\",\"authors\":\"Jing Zhang, Jin Shi, Jingsheng Duan, Yonggong Ren\",\"doi\":\"10.1007/s10115-024-02165-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model’s sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step <i>T</i>, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. 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引用次数: 0
摘要
增量推荐是指通过从当前时间步骤的交互数据中提取信息来更新现有模型,目的是在保持模型准确性的同时解决参数依赖性和训练效率低下等限制因素。然而,实时用户交互数据往往存在大量噪声和无效样本,这给增量模型更新带来了以下关键挑战:(1) 如何在当前时间步有效地从交互数据中提取有价值的新知识,以确保模型的准确性和及时性;(2) 如何防止长期稳定偏好信息的灾难性遗忘,从而在冷启动时保持模型的灵敏度。为了应对这些挑战,我们提出了稳定潜在侧面信息更新增量推荐模型(SIIFR)。该模型利用侧信息增强器从时间步 T 的用户交互行为中提取有价值的潜在侧信息,从而避开噪声交互数据的干扰,获得稳定的用户偏好。此外,该模型还利用时间步(T+1)的粗略交互数据,结合现有的侧信息增强器,实现潜在偏好的增量更新,从而确保模型在冷启动期间的有效性。此外,SIIFR 还能利用用户潜在侧信息的变化率来减轻灾难性遗忘导致的长期稳定偏好信息丢失。我们使用四种流行的增量数据集对所提出模型的有效性进行了验证,并与现有模型进行了比较。模型代码见:https://github.com/LNNU-computer-research-526/FR-sii。
Latent side-information dynamic augmentation for incremental recommendation
The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model’s sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step T, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step \(T+1\), in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model’s efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii.
期刊介绍:
Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership.