Deep Intention-Aware Network for Click-Through Rate Prediction

• URL: http://arxiv.org/abs/2211.08650v1
• Date: Wed, 16 Nov 2022 03:55:18 GMT
• Title: Deep Intention-Aware Network for Click-Through Rate Prediction
• Authors: Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu, Lingling Lu
• Abstract summary: Trigger items displayed on entrance icons can attract more entering. Traditional Click-Through-Rate (CTR) prediction models ignore user instant interest in trigger item. Deep Intention-Aware Network (DIAN) can both accurately predict user intention and dynamically balance the results.
• Score: 9.00554150844311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of customers to mini-apps, we argue that existing trigger-based methods that over-emphasize the importance of trigger items, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user's entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user's intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations based on the estimated intention. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.

Related papers

• 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)
• ClickTrack: Towards Real-time Interactive Single Object Tracking [58.52366657445601]
  We propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. To address ambiguity in certain special scenarios, we designed the Guided Click Refiner(GCR), which accepts point and optional textual information as inputs. Experiments on LaSOT and GOT-10k benchmarks show that tracker combined with GCR achieves stable performance in real-time interactive scenarios.
  arXiv  Detail & Related papers  (2024-11-20T10:30:33Z)
• Collaborative Contrastive Network for Click-Through Rate Prediction [21.19413982573744]
  We introduce a more general and robust CTR prediction approach, dubbed Collaborative Contrastive Network (CCN) CCN learns to identify two item clusters that can represent the user's interests and disinterests. Online A/B testing on large-scale real-world data demonstrates that CCN sets a new state-of-the-art performance on Taobao, boosting CTR by 12.3% and order volume by 12.7%.
  arXiv  Detail & Related papers  (2024-11-18T12:12:47Z)
• Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation [11.699080619946045]
  We propose a novel model called Deep Uncertainty Intent Network (DUIN) DUIN consists of three essential modules: Explicit Intent Exploit Module, Latent Intent Explore Module and Intent Uncertainty Measurement Module. DUIN has been deployed across all Trigger-Induced Recommendation scenarios in our e-commerce platform.
  arXiv  Detail & Related papers  (2024-08-06T10:45:14Z)
• Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions [45.43816499513853]
  EmerG is a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG consistently performs the best given no, a few and sufficient instances of new items.
  arXiv  Detail & Related papers  (2024-07-14T07:58:13Z)
• Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation [28.29435760797856]
  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.
  arXiv  Detail & Related papers  (2024-01-15T15:27:24Z)
• Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning [60.64535309016623]
  We propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision. We further introduce a incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-05-09T05:08:08Z)
• 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)
• End-to-End User Behavior Retrieval in Click-Through RatePrediction Model [15.52581453176164]
  We propose a locality-sensitive hashing (LSH) method called ETA which can greatly reduce the training and inference cost. We deploy ETA into a large-scale real world E-commerce system and achieve extra 3.1% improvements on GMV (Gross Merchandise Value) compared to a two-stage long user sequence CTR model.
  arXiv  Detail & Related papers  (2021-08-10T06:28:29Z)
• Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation [66.8722561224499]
  We propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach.
  arXiv  Detail & Related papers  (2020-12-12T22:10:15Z)
• ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking [80.02322563402758]
  One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets. We introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion.
  arXiv  Detail & Related papers  (2020-04-16T06:43:11Z)

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.