The video tutorial explains the inference mechanism in recommendation systems, focusing on three key steps: candidate generation, candidate ranking, and filtering. Candidate generation involves pairing users with potential items based on user-item similarity. Candidate ranking assesses the likelihood of user interest in these items. Filtering then selects the items most likely to be enjoyed by the user. The tutorial concludes with a brief mention of deep learning models in this context.

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