Personalized Recommendation Techniques And Applications Based On User Decision-making Behaviors

Personalized recommendation technology refers to recommending products that meet users’ own interests,preferences and intentions in online recommendation application scenarios.In recent years,with the rapid development of Internet-related technologies and industries,recommender systems have been widely used in online service scenarios such as online shopping platforms,stream media apps,and music websites.On the one hand,the recommender systems can improve user experience and stickiness.On the other hand,it can also increase product exposure and improve the efficiency of content distribution,which has extremely high commercial value.Therefore,recommender system has become a long-term popular research problem in computer science,management and related interdisciplinary subjects.It has attracted the attention of many researchers.However,the existing recommender systems still face the difficulty of understanding the user’s intentions and portraits,the complex and changeable preference modeling,and the unquantifiable recommendation confidence evaluation.In management filed,the decision-making process of a user is divided into five stages:need recognition,information search,alternatives evaluation,purchase decision and decision evaluation.To improve the user experience in these five decision-making stages,the recommender system needs to face three main research problems:user understanding,decision understanding,and decision evaluation.To this end,this paper uses machine learning,data mining and other technologies to systematically perform research for personalized recommendation around the five stages in the user decision-making process to improve user experience.For these five stages,the disentangled user decision-making intention modeling method,user portrait modeling method,setwise preference ranking modeling method,time-aware sequential decision modeling method,and user decisionmaking confidence modeling method are respectively proposed.The main work and contributions of this paper can be summarized as follows:First,it is important to construct comprehensive user understanding through modeling user intentions and portraits.On the one hand,in the need recognition stage,we propose a user intention modeling approach.In traditional collaborative filtering approaches,both intention and preference factors are usually entangled in the modeling process,which significantly limits the robustness and interpretability of recommendation performances.To this end,we propose a double disentangled collaborative filtering(DDCF)approach for personalized recommendations.The first-level disentanglement is for separating the influence factors of intention and preference,while the second-level disentanglement is performed to build independent sparse preference representations under individual intention with limited computational complexity.Finally,extensive experiments on three real-world datasets clearly validate the effectiveness and the interpretability of DDCF.On the other hand,in the information search stage,we present a focused study on the explainable user portrait modeling method.We take the employee training course recommendation problem as an example to show how to perform user portrait modeling.Specifically,we jointly model both the employees’ current competencies and their career development preferences in an explainable way.First,we extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling.Then,we develop an effective demand recognition mechanism for learning the personal demands of career development for employees.Finally,we can generate explainable recommendation results based on the competency representations.Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks,as well as their robustness on sparse and cold-start scenarios.Second,it is important to construct deep decision understanding for single and multiple decisions.On the one hand,in the alternatives evaluation stage,we study the preference ranking modeling problem.While considerable efforts have been made in this direction,the well-known pairwise and listwise approaches have still been limited by various challenges.Specifically,for the pairwise approaches,the assumption of independent pairwise preference is not always held in practice.Also,the listwise approaches cannot efficiently accommodate items with the same rating value and unobserved data due to the precondition of the entire list permutation.To this end,we propose a novel setwise Bayesian approach for collaborative ranking,namely SetRank,to inherently accommodate the characteristics of user feedback in recommender systems.Finally,extensive experiments on three real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.On the other hand,in the purchase decision stage,we propose a time-aware sequential decision-making modeling approach.The dependence between multiple decision sequences must be considered in the decision-making process.Here we take the career trajectory prediction problem as an example.Specifically,we first exploit a hierarchical deep sequential modeling network for career embedding.Furthermore,we propose a temporal encoding mechanism to handle dynamic temporal information so that we can generate time-aware predictions by addressing the challenges for variable interval time sequence modeling.Finally,we have conducted extensive experiments on large-scale real-world data,and the results show that our approach has advantages on all tasks.Third,in the decision evaluation stage,we propose a Confidence-aware Matrix Factorization(CMF)framework to simultaneously optimize the accuracy of rating prediction and measure the prediction confidence in the model.Specifically,we introduce variance parameters for both users and items in the matrix factorization process.Then,prediction interval can be computed to measure confidence for each predicted rating.These confidence quantities can be used to enhance the quality of recommendation results based on Confidence-aware Ranking(CR).We also develop two effective implementations of our framework to compute the confidence-aware matrix factorization for large-scale data.Finally,extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives.