When confronted with too many choices, our satisfaction decreases (Iyengar & Lepper, 2000). Recommender systems may help increase satisfaction by making certain items more salient than others. Content-based recommendations are based on previouslyB FerwerdaK SwelsenE YangBruceferwerda Com...
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Content-based filtering example If we talk about movie recommendations, for example, the attributes may be the length of a film, its genre, cast, director, and so on.Say, a user has watched such movies as “Heat,”“Goodfellas,” and“The Irishman.” A content-based system will probably...
however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an ...
(ACF) systems predict a recommendations for the user. person’s affinity for items or information by connecting that ACF has many significant advantages over traditional person’s recorded interests with the recorded interests of a content-based filtering, primarily because it does not depend ...
text, art work, music, mutual funds; the ability to filterbased on complex and hard to represent concepts, such astaste and quality; and the ability to make serendipitousrecommendations.It is important to note that ACF technologies do notnecessarily compete with content-based filtering. In most...
Most of the researchin recommender systems has focused on efficient and accu-rate algorithms for computing recommendations using meth-ods such as collaborative filtering [4, 5], content-based clas-sifier induction [9, 8], and hybrids of these two techniques[1, 7]. However, in order for ...
Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to ...
Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such...
Governments worldwide have intensified their efforts to institutionalize policy evaluation. Still, also in organizations with high evaluation maturity, the use of evaluations is not self-evident. As mature organizations already meet many of the factors t