A Study Of Dynamic User Intention For Recommendation With Implicit Feedback

Recommender system has become an important tool for obtaining information and assisting decision-making in various scenarios.To provide precise recommendation services,it is critical to understand user intentions accurately.However,it is common that only implicit feedback data(e.g.,click,browse)are available in real-world systems.The current user intention can hardly be obtained directly.Therefore,how to better understand users’ dynamic intentions behind implicit feedback has become an important issue for implicit recommendation,which brings many challenges.First of all,there is a lack of explicit feedback on users’ actual intentions,and existing recommendation models lack in-depth understanding and dynamic modeling of relevant influencing factors.Secondly,the implicit feedback data lacks real negative examples,and existing learning algorithms based on random sampling can hardly ensure that all the negative samples are in line with user intentions.Finally,recommendation results that blindly optimize implicit feedback may not cater to users’ intentions,which can easily lead to problems such as clickbait and domination of low-quality items.To tackle the above challenges,this dissertation conducts research on implicit recommendation methods based on dynamic user intention from three perspectives:From the perspective of user modeling,this dissertation tries to understand the dynamic intentions behind user behaviors,finding internal relationships between implicit feedback.First,this dissertation analyzes the repeat consumption behavior for a single item and models its self-excitation effects through Hawkes process.Further,considering the more complex interplay between different items,this dissertation leverages the knowledge graph to help understand the connections between user interactions.Based on the frequency domain embedding inspired by Fourier transform,this dissertation also achieves the adaptive modeling of the dynamic temporal effects of item relations.The final model improves the ranking performance of the benchmark algorithms by 13% on the Amazon dataset.From the perspective of model training,this dissertation explores self-supervised signals in implicit feedback data,constructing reasonable objectives without relying on negative sampling.On the one hand,this dissertation proposes a representation learning algorithm based on alignment and uniformity,which directly optimizes the desired properties of representations in recommendation.This learning algorithm is significantly better than the common random negative sampling training paradigm.On the other hand,this dissertation proposes a sequential contrastive learning algorithm based on intention invariance modeling to improve the quality of sequence representation.As an auxiliary loss function,the proposed learning algorithm leads to more than 10% performance improvements when combined with various sequential recommendation models.From the perspective of system presenting,this dissertation tries to introduce the item content quality to rerank the original recommendation list based on implicit feedback optimization,aiming to satisfy the user demands for responsible recommendation.In particular,this dissertation proposes the quality-aware exposure regulation task and designs a reranking algorithm based on personalized target exposure quality.Compared with the benchmark reranking algorithms,it can better balance the ranking performance,fairness,and other aspects in recommendation while ensuring the reasonable exposure of high-quality items.Towards dynamic user intention behind implicit feedback,this dissertation conducts research from the perspective of user modeling,model training,and system presenting.The proposed methods involve each stage of the feedback loop in personalized recommendation,which can comprehensively and systematically improve the recommendation performance in real-world applications.