Recommender Systems

Collaborative approaches infer preferences from patterns across users and items, while content-based approaches use attributes of items and users. Hybrid systems combine these signals, often through a retrieval stage followed by ranking. Explicit ratings and implicit events such as clicks provide different evidence: an unclicked item may never have been shown. Training objectives can predict ratings or optimize relative rankings, and contextual or sequential models account for the current situation. A recommender also imposes a policy about what becomes visible. Its predictions and the data it later observes are connected through exposure, which complicates evaluation and can reinforce existing patterns.

Define the recommendation surface and what useful means, then establish popularity and content-based baselines. Record exposures where possible, choose training examples and negative sampling carefully and split data according to intended future use. Evaluate ranking quality alongside coverage, diversity and important user or item slices. Account for new users and items, and inspect the consequences of repeated recommendations. The result includes candidate and ranking logic plus an evaluation plan that distinguishes offline prediction from actual improvement under the recommendation policy.

Interaction data reflect earlier exposure and selection, not pure preference. Cold starts, feedback loops and popularity bias can weaken results. Offline ranking metrics depend strongly on negative sampling and the candidate set; numbers from different protocols are not directly comparable. Optimizing clicks can conflict with satisfaction or longer-term goals. Recommendation differs from ordinary classification because items compete for limited attention, so evaluation should examine the ranked slate and the behavior of the complete serving policy.