Multi-criteria based Item Recommendation Methods
نویسندگان
چکیده
منابع مشابه
Supervised Machine Learning Methods for Item Recommendation
class, 107accuracy, 40AdaBoost, 77adaptivity, 41age, 81ALS, see alternating least squaresalternating least squares, 36, 86Apache Mahout, 86area under the ROC curve, 41, 61, 82aspect model, 58association rules, 87attribute-based kNN, 81attribute-to-factor mapping, 45 59AUC, see area under the ROC curve bagging, 77, 87Bayesian Context-Aware ...
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Web recommendation services bear great importance in ecommerce, as they aid the user in navigating through the items that are most relevant to her needs. In a typical Web site, long history of previous activities or purchases by the user is rarely available. Hence in most cases, recommenders propose items that are similar to the most recent ones viewed in the current user session. The correspon...
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Recommendation systems utilize data analysis techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering ...
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Article history: Received 23 February 2010 Received in revised form 7 February 2011 Accepted 7 February 2011 Available online 15 February 2011 Communicated by J. Chomicki
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Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
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ژورنال
عنوان ژورنال: Rekayasa
سال: 2019
ISSN: 2502-5325,0216-9495
DOI: 10.21107/rekayasa.v12i2.5913