为了满足用户对沉浸式浏览体验的需求，众多电子商务平台提供了各种推荐场景，并重点关注触发诱导推荐（Trigger-Induced Recommendation, TIR）任务。然而，当前大多数 TIR 方法严重依赖触发项来理解用户意图，缺乏对用户意图更高层次的探索与利用（例如热门商品和互补商品），这可能导致对用户短期意图的理解过于收敛，并损害用户的长期购买体验。此外，用户的短期意图表现出不确定性，并受到浏览上下文和历史行为等多种因素的影响，这给用户意图建模带来了挑战。为了解决这些问题，我们提出了一种名为深度不确定性意图网络（Deep Uncertainty Intent Network, DUIN）的新模型，该模型包含三个核心模块：i) 显式意图利用模块，利用对比学习范式提取显式用户意图；ii) 潜在意图探索模块，通过利用商品之间的多视图关系探索潜在用户意图；iii) 意图不确定性测量模块，提供分布估计并捕捉与用户意图相关的不确定性。在三个真实世界数据集上的实验表明，与现有基线相比，DUIN 具有更优越的性能。值得注意的是，DUIN 已部署在我们电子商务平台的所有 TIR 场景中，在线 A/B 测试结果最终验证了其优越性。
英文摘要：
To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendation (TIR) tasks. However, the majority of current TIR methods heavily rely on the trigger item to understand user intent, lacking a higher-level exploration and exploitation of user intent (e.g., popular items and complementary items), which may result in an overly convergent understanding of users' short-term intent and can be detrimental to users' long-term purchasing experiences. Moreover, users' short-term intent shows uncertainty and is affected by various factors such as browsing context and historical behaviors, which poses challenges to user intent modeling. To address these challenges, we propose a novel model called Deep Uncertainty Intent Network (DUIN), comprising three essential modules: i) Explicit Intent Exploit Module extracting explicit user intent using the contrastive learning paradigm; ii) Latent Intent Explore Module exploring latent user intent by leveraging the multi-view relationships between items; iii) Intent Uncertainty Measurement Module offering a distributional estimation and capturing the uncertainty associated with user intent. Experiments on three real-world datasets demonstrate the superior performance of DUIN compared to existing baselines. Notably, DUIN has been deployed across all TIR scenarios in our e-commerce platform, with online A/B testing results conclusively validating its superiority.
Sequential Recommendation (SRs) that capture users' dynamic intents by modeling user sequential behaviors can recommend closely accurate products to users. Previous work on SRs is mostly focused on optimizing the recommendation accuracy, often ignoring the recommendation diversity, even though it is an important criterion for evaluating the recommendation performance. Most existing methods for improving the diversity of recommendations are not ideally applicable for SRs because they assume that user intents are static and rely on post-processing the list of recommendations to promote diversity…
Re-ranking models refine item recommendation lists generated by the prior global ranking model, which have demonstrated their effectiveness in improving the recommendation quality. However, most existing re-ranking solutions only learn from implicit feedback with a shared prediction model, which regrettably ignore inter-item relationships under diverse user intentions. In this paper, we propose a novel Intention-aware Re-ranking Model with Dynamic Transformer Encoder (RAISE), aiming to perform user-specific prediction for each individual user based on her intentions. Specifically, we first pro…
Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recommender systems. In this paper, we propose a dynamic intention-aware recommender system to better facilitate users to find desirable products and services. Compare to prior work, our proposal possesses the following advantages: (1) it takes user intentions and demands into account through intention mining techniques. By unearthing user intentions from the historical user-item interactions, and various user digital trac…
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation. There is a common scenario that user specifies a target category of items as a global filter, however previous SBR settings mainly consider the item sequence and overlook the rich target category information. Therefore, we define a new task called Category-aware Session-Based Recommendation (CSBR), focusing on the above scenario, in which the user-specified category can be efficiently uti…
One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., co…
The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences) on websites remains a big challenge for predicting users' behaviors. Although research efforts have been made in utilizing some auxiliary information (e.g., social relations between users) to solve the problem, the existing rich heterogeneous auxiliary relationships are still not fully exploited. Moreover, previous works relied on linearly combined regularizers and suffered parameter tuning. In this work, we collect …
Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation
Sydney Smart Technology College, Northeastern University（悉尼智能科技学院，东北大学）；School of Computer and Communication Engineering and Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao（秦皇岛东北大学计算机与通信工程学院及河北省海洋感知网络与数据处理重点实验室）；Shijiazhuang Innovation Research Institute of Northeastern University（东北大学石家庄创新研究院）
Sequential recommendation recommends items based on sequences of users' historical actions. The key challenge in it is how to effectively model the influence from distant actions to the action to be predicted, i.e., recognizing the long-term dependence structure; and it remains an underexplored problem. To better model the long-term dependence structure, we propose a GatedLongRec solution in this work. To account for the long-term dependence, GatedLongRec extracts distant actions of top-$k$ related categories to the user's ongoing intent with a top-$k$ gating network, and utilizes a long-term …
Session-based recommendation aims to predict a user's next action based on previous actions in the current session. The major challenge is to capture authentic and complete user preferences in the entire session. Recent work utilizes graph structure to represent the entire session and adopts Graph Neural Network to encode session information. This modeling choice has been proved to be effective and achieved remarkable results. However, most of the existing studies only consider each item within the session independently and do not capture session semantics from a high-level perspective. Such l…
SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
University of Georgia（佐治亚大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Education University of Hong Kong（香港教育大学）；Jilin University（吉林大学）；The Hong Kong Polytechnic University（香港理工大学）
University of Technology Sydney（悉尼科技大学）；Curtin University（科廷大学）；University of New South Wales（新南威尔士大学）；Tsinghua University（清华大学）
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbia…
In this work, we aim to learn multi-level user intents from the co-interacted patterns of items, so as to obtain high-quality representations of users and items and further enhance the recommendation performance. Towards this end, we develop a novel framework, Hierarchical User Intent Graph Network, which exhibits user intents in a hierarchical graph structure, from the fine-grained to coarse-grained intents. In particular, we get the multi-level user intents by recursively performing two operations: 1) intra-level aggregation, which distills the signal pertinent to user intents from co-intera…
Huazhong University of Science and Technology（华中科技大学）；Beijing Institute of Technology（北京理工学院）
Bundle recommendation aims to recommend the user a bundle of items as a whole. Nevertheless, they usually neglect the diversity of the user's intents on adopting items and fail to disentangle the user's intents in representations. In the real scenario of bundle recommendation, a user's intent may be naturally distributed in the different bundles of that user (Global view), while a bundle may contain multiple intents of a user (Local view). Each view has its advantages for intent disentangling: 1) From the global view, more items are involved to present each intent, which can demonstrate the us…
For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect a user's current preference, a local shift of the user's intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user's initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability…
Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user's home-town preference but also the user's travel intention. Besides, the user's travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The…
End-to-end Learnable Clustering for Intent Learning in Recommendation
Ant Group（蚂蚁集团）；National University of Singapore（新加坡国立大学）；Westlake University（西湖大学）；Alibaba Group（阿里巴巴集团）；National University of Defense Technology（国防科技大学）；Zhejiang University（浙江大学）
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
School of Information Science and Engineering, Yanshan University（燕山大学信息科学与工程学院）；School of Information Science and Engineering, Xinjiang College Of Science & Technology（新疆科学技术学院信息科学与工程学院）
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
College of Computer, Qinghai Normal University（青海师范大学计算机学院）；The State Key Laboratory of Tibetan Intelligent Information Processing and Application（藏语智能信息处理及应用国家重点实验室）；Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province（青海省藏文信息处理与机器翻译重点实验室）；Key Laboratory of Tibetan Information Processing, Ministry of Education（藏文信息处理教育部重点实验室）
Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations
College of Computer, Nanjing University of Posts and Telecommunications（南京邮电大学计算机学院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）；University of Chinese Academy of Sciences（中国科学院大学）
University of California, San Diego（加州大学圣地亚哥分校）；Amazon（亚马逊）
Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely …
