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...
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...
A new method for estimation of age-at-death based on the degree of suture closure is presented. The method employs simple ectocranial scoring of specific s... RS Meindl,CO Lovejoy - 《American Journal of Physical Anthropology》 被引量: 1272发表: 2010年 Recommendations for age and sex diagno...
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 ...
However, those GRS that support explanations, provide either explanations be-ing highly reliant on the aggregation technique used for generating the recommendation (most of them trying to tackle shortcomings of the underlying tech-nique), or explanations with a rich content but requiring users to ...
(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 ...
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 ...
Our proposed methods are based on subgroup discovery with different pattern languages (i.e., itemsets and sequences). Specifically, we provide interpretable explanations of the recommendations of the Top-N items, which are useful to compare different models. Ultimately, these can then 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...
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