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Recommender Systems
(Summarized Notes from Recommender Systems (intro chap) + Survey Papers) The basic idea in Recommender systems is to use different sources of feedback data to infer customer interests. This feedback can be direct – like user ratings (likes/ dislikes/ 1-5 etc) or implicit – based on click or purchasing behavior. In the most common formulation,…
The Basic Outlier Detection Models
(Notes from Outlier Analysis – by Charu Aggarwal) Factors influencing choice of outlier model: Data Type Data Size Availability of Labeled outliers Need for interpretability (Very desirable) Interpretability of Model Results A model that can describe why a particular data point is considered an outlier could provide the analyst further hints about the diagnosis and…
Outlier Analysis: The Data model is everything
(Notes from Outlier Analysis Chap1: by Charu C Aggarwal) All outlier detection algorithms generally follow this approach: Create a model of normal patterns in the data For given data point, compute outlier score based on deviations from this pattern. This is done by evaluating the quality of the fit between the data point and the…
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