Rankingmallit
Rankingmallit are statistical or machine learning models that produce an ordered list of items according to predicted relevance, quality, or utility. They are used to present search results, product lists, news feeds, and recommendations in a way that the most relevant items appear first. Models are typically trained from labeled data indicating the relevance of items to queries, users, or contexts; they may also leverage implicit feedback such as clicks or dwell time.
Ranking approaches are commonly categorized by how they treat the ranking problem: pointwise, pairwise, and listwise.
Algorithms and architectures include traditional machine learning models such as ranking SVMs and gradient boosting methods,
Evaluation uses metrics that quantify the quality of the ordered results, such as normalized discounted cumulative
Applications span search engines, e-commerce, news aggregators, and recommender systems. Data for training can be explicit
Limitations include dependence on high-quality labeled data, sparsity, bias in feedback data, and computational demands for