Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation

• URL: http://arxiv.org/abs/2401.07769v3
• Date: Sun, 4 Aug 2024 14:46:14 GMT
• Title: Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
• Authors: Zhibo Xiao, Luwei Yang, Tao Zhang, Wen Jiang, Wei Ning, Yujiu Yang,
• Abstract summary: We propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N) -- for click-through rate prediction in TIR scenarios. We design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. We evaluate our method on several offline and real-world industrial datasets.
• Score: 28.29435760797856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon. Without explicitly modeling the user's instant interest, traditional recommendation methods usually obtain sub-optimal results in TIR. Even though there are a few methods considering the trigger and target items simultaneously to solve this problem, they still haven't taken into account temporal information of user behaviors, the dynamic change of user instant interest when the user scrolls down and the interactions between the trigger and target items. To tackle these problems, we propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N), for click-through rate prediction in TIR scenarios. Specifically, we design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. Temporal information is utilized in user behavior modeling. Moreover, an Interaction Layer is introduced to learn better interactions between the trigger and target items. We evaluate our method on several offline and real-world industrial datasets. Experimental results show that our proposed DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing demonstrates the superiority over the existing baseline in real-world production environments.

Related papers

• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph [18.826901341496143]
  Temporal event forecasting is a new user behavior prediction task in a unified query product evolutionary graph. We propose a novel RetrievalEnhanced Event forecasting framework. Unlike existing methods, we propose methods that enhance user representations via roughly connected entities in the whole graph.
  arXiv  Detail & Related papers  (2022-02-12T19:27:56Z)
• Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation [15.490873353133363]
  We present a new recommendation problem, Trigger-Induced Recommendation (TIR), where users' instant interest can be explicitly induced with a trigger item. To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction. It has three main components including 1) User Intent Network (UIN), which responds to generate a precise probability score to predict user's intent on the trigger item; 2) Fusion Embedding Module (FEM), which adaptively fuses trigger item and target item embeddings based on the prediction from UIN; and (3)
  arXiv  Detail & Related papers  (2022-02-05T08:40:30Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users [3.464871689508835]
  Implicit and explicit feedback have different roles for a useful recommendation. We go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests.
  arXiv  Detail & Related papers  (2021-07-26T16:59:20Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)
• Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction [48.267995749975476]
  In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest. Existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction. We propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features.
  arXiv  Detail & Related papers  (2020-12-13T05:46:19Z)

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.