Regularization Methods for Multi - Output Learning
نویسندگان
چکیده
• Suppose we are attempting to model the buying preferences of several consumers based on past purchases, e.g. as in the Netflix recommender system. We assume that people with similar tastes tend to buy similar items and their buying history is related. Inferring the preferences for a customer based only on his past purchases may be tough, because that customer may not have rated enough movies or made enough purchases. If we can add information from other customers with similar tastes, then we can essentially increase the number of data samples and hopefully also increase classification accuracy, thereby prompting customers to rent movies attuned to their tastes and be more satisfied with the overall Netflix service. In this case, each consumer is modeled as a task and their previous preferences are the corresponding training set.
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