Temporal Interest Network for User Response Prediction

• URL: http://arxiv.org/abs/2308.08487v4
• Date: Wed, 3 Jul 2024 23:28:10 GMT
• Title: Temporal Interest Network for User Response Prediction
• Authors: Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen,
• Abstract summary: We propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. TIN has been successfully deployed in production since October 2023, serving the WeChat Moments traffic.
• Score: 33.95334897033641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavior reflects her interest in the candidate item, owing to the semantic or temporal correlation between behaviors and the candidate. While the literature has individually examined each of these correlations, researchers have yet to analyze them in combination, that is, the semantic-temporal correlation. We empirically measure this correlation and observe intuitive yet robust patterns. We then examine several popular user interest models and find that, surprisingly, none of them learn such correlation well. To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic encoding, to represent behaviors and the target. Furthermore, we conduct explicit 4-way interaction by deploying target-aware attention and target-aware representation to capture both semantic and temporal correlation. We conduct comprehensive evaluations on two popular public datasets, and our proposed TIN outperforms the best-performing baselines by 0.43% and 0.29% on GAUC, respectively. During online A/B testing in Tencent's advertising platform, TIN achieves 1.65% cost lift and 1.93% GMV lift over the base model. It has been successfully deployed in production since October 2023, serving the WeChat Moments traffic. We have released our code at https://github.com/zhouxy1003/TIN.

Related papers

• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences. This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Long-Sequence Recommendation Models Need Decoupled Embeddings [49.410906935283585]
  We identify and characterize a neglected deficiency in existing long-sequence recommendation models. A single set of embeddings struggles with learning both attention and representation, leading to interference between these two processes. We propose the Decoupled Attention and Representation Embeddings (DARE) model, where two distinct embedding tables are learned separately to fully decouple attention and representation.
  arXiv  Detail & Related papers  (2024-10-03T15:45:15Z)
• Spuriousness-Aware Meta-Learning for Learning Robust Classifiers [26.544938760265136]
  Spurious correlations are brittle associations between certain attributes of inputs and target variables. Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold. Mitigating the impact of spurious correlations is crucial towards robust model generalization, but it often requires annotations of the spurious correlations in data.
  arXiv  Detail & Related papers  (2024-06-15T21:41:25Z)
• Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction [32.35797628133921]
  Deep Group Interest Network (DGIN) is an end-to-end method to model the user's entire behavior history. DGIN grouping reduces the behavior length significantly, from O(104) to O(102) We identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence.
  arXiv  Detail & Related papers  (2023-11-15T06:36:11Z)
• Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models [0.0]
  We propose a novel neighbor enhancement structure to help train the representation of the target user or item. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method.
  arXiv  Detail & Related papers  (2023-02-16T07:38:51Z)
• DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction [53.76601630407521]
  We propose a hypergraph network model called DisenHCN to bridge the gaps in existing solutions. In particular, we first unify fine-grained user similarity and the complex matching between user preferences andtemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph.
  arXiv  Detail & Related papers  (2022-08-14T06:51:54Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• Dynamic Sequential Graph Learning for Click-Through Rate Prediction [29.756257920214168]
  We propose a novel method to enhance users' representations by utilizing collaborative information from the local sub-graphs associated with users or items. Results on real-world CTR prediction benchmarks demonstrate the improvements brought by DSGL.
  arXiv  Detail & Related papers  (2021-09-26T09:23:43Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.