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Context: Later, we will show that a generalized Expectation-Maximization frameworkprovides a direction to address above problemwith a convergence guarantee.The basic idea of optimizing Eq. (7) via EMis to start with an initial guessof the model parameter \thetaand estimate the expected valuesof the missing variable c, i.e., the E-step.And once we have the values of c,we can maximize the Eq. (7) w.r.t theparameter \theta, i.e., the M step.We can repeat this iterative process until the likelihood cannot increase anymore. To discover the benefits oflatent intentsand address challenges,we propose theIntent Contrastive Learning (ICL),a general learning paradigm thatleverages the latent intentfactor into SR.It learns users’ intentdistributionsfrom all user behavior sequencesvia clustering.And it leveragesthe learnt intentsinto the SR modelvia a new contrastive SSL,whichmaximizes the agreementbetween a view of sequenceand its corresponding intent.The intent representation learning moduleand the contrastive SSL module are mutually reinforcedto train a more expressivesequence encoder.We tackle the challenge of intentmining problem byintroducing alatent variable to represent users’ intentsand learn them alternatelyalong with the SR model optimization throughan expectation-maximization (EM) frameworkto ensure convergence.We suggest fusing learnt intent informationinto SR via the proposed contrastive SSL,as it can improve model’s performance as wellas robustness.Extensive experiments conducted on four real-world datasetsfurther verify the effectiveness of the proposed learning paradigm,which improves performance and robustness,even when recommender systemsfaceheavy data sparsity issues. In this work,we propose a new learning paradigm ICL that canmodel latent intent factorsfrom user interactionsand fuse them into a sequential recommendationmodel via a newcontrastive SSL objective.ICL is formulated withinan EM framework, which guaranteesconvergence. Detailed analyses showthe superiority of ICL andexperiments conducted onfour datasets further demonstratethe effectiveness of the proposed method.

Question: Why does the proposed method introduced EM framework to optimize the model (instead of directly optimizing the loss)?
EM guarantees convergence.