Active Learning Recommender Systems by Karimi Rasoul (17 results)
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contact seller5-star sellerCondition: Used - As new
£ 23.27
£ 1.95 shippingShips within U.S.A.Quantity: Over 20 available
Condition: As New. Unread book in perfect condition.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contact seller5-star sellerCondition: New
£ 23.79
£ 1.95 shippingShips within U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
Contact seller5-star sellerCondition: New
£ 26.37
Free ShippingShips within U.S.A.Quantity: 5 available
Paperback or Softback. Condition: New. Active Learning for Recommender Systems. Book.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
Contact seller4-star sellerCondition: New
£ 26.61
Free ShippingShips within U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
Contact seller5-star sellerCondition: New
£ 22.07
£ 11.98 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New. In.
- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
Contact seller5-star sellerCondition: New
£ 19.52
£ 15.49 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Paperback. Condition: New.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: New
£ 22.00
£ 15.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: Used - As new
£ 25.08
£ 15.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: As New. Unread book in perfect condition.
- Softcover
Seller: Buchpark, Trebbin, GermanyBuchpark
Contact seller5-star sellerCondition: Used
£ 20.03
£ 89.71 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really help…ful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests. The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems. The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items. Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.
- Softcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: New
£ 100.00
£ 25.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Softcover
- Print on Demand
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
Contact seller5-star sellerCondition: New
£ 25.81
Free ShippingShips within U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
£ 23.56
£ 4.16 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 24.29
£ 19.65 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be rea…lly helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches. 152 pp. Englisch.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 22.08
£ 41.86 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorrnrnRasoul Karimi was born in 1980 in Tehran. He studied computer engineering and got his master degree in 2005 from the University of Tehran. He started his PhD in 2009 in Information System and Mac…hine Learning Lab (ISMLL), .
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 24.29
£ 51.26 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really…helpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 152 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 26.00
£ 52.15 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays we are living in an era that is overloaded with information. Decision-making in this environment can sometimes become a nightmare. There are too many choices and we simply cannot explore them all. Therefore, it would be really h…elpful to have a system to help us to find the right choice. Such systems, which learn user preferences and provide personalized recommendations to them are called Recommender Systems.Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new user problem or cold-start problem. A simple and effective way to overcome this problem, is by posing queries to new users so that they express their preferences about selected items, e.g. by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e ratings from users that will help most in determining their interests.The aim of this thesis is to take inspiration from the literature of active learning for machine learning and develop new methods for the new user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide labels (ratings) to the queries (items). In this approach, we will take into consideration that although there are no data for new users, but there is abundant data for existing users. Such additional data can help us to develop scalable and accurate active learning methods for the new user problem in recommender systems.The thesis consists of two parts. In the first part, to be consistent with the settings of active learning in machine learning and the related works on the new user problem in recommender system, it is assumed that the new user is always able to rate the queried items. Next, this constraint is relaxed and new users are allowed not to rate the items.Most of the developed active learning methods exploit the characteristics matrix factorization because nevertheless, recent research (especially as has been demonstrated during the Netflix challenge) indicates that matrix factorization is a superior prediction model for recommender systems compared to other approaches.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
£ 22.18
£ 59.81 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Active Learning for Recommender Systems | Rasoul Karimi | Taschenbuch | 152 S. | Englisch | 2014 | Cuvillier | EAN 9783954046928 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.
