Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User behavior sequence modeling has marked progress in CTR prediction, which extracts users' latent interests from their historical behavior sequences to facilitate accurate CTR prediction. Recent research explores using implicit feedback sequences, like unclicked records, to extract diverse user interests. However, these methods encounter key challenges: 1) temporal misalignment due to disparate sequence time ranges and 2) the lack of fine-grained interaction among feedback sequences. To address these challenges, we propose a novel framework called TEM4CTR, which ensures temporal alignment among sequences while leveraging auxiliary feedback information to enhance click behavior at the item level through a representation projection mechanism. Moreover, this projection-based information transfer module can effectively alleviate the negative impact of irrelevant or even potentially detrimental components of the auxiliary feedback information on the learning process of click behavior. Comprehensive experiments on public and industrial datasets confirm the superiority and effectiveness of TEM4CTR, showcasing the significance of temporal alignment in multi-feedback modeling.
Code (0)
등록된 구현이 없습니다.
Tasks
Click-Through Rate PredictionPredictionRecommendation SystemsSimilar Papers 제목 키워드 기반
USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations
Large-scale homepage recommendations face critical challenges from pseudo-negative samples caused by exposure bias, where non-clicks may indicate inattention rather than disinterest. Existing work lacks thorough analysis…
MarketingChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typical CVR positive sample usually goes thro…
counterfactualRecommendation SystemsSelection biasAn Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how users interact with a ranked list of items,…
Imitation LearningInformation RetrievalmodelRecommendation Systems+1Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on t…
Click-Through Rate PredictioncounterfactualCounterfactual InferenceESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
Large-scale online recommender system spreads all over the Internet being in charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion Rate (CVR) estimations. However, traditional CVR estimators suffe…
Decision MakingRecommendation SystemsSelection bias