Results 291 to 300 of about 208,309 (312)

Listwise Collaborative Filtering

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do).
Huang, Shanshan, Wang, Shuaiqiang, Liu, Tie-Yan, Ma, Jun, Chen, Zhumin, Veijalainen, Jari   +6 more
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Nantonac collaborative filtering

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03, 2003
A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies similar to the user preference. Traditionally, the degree of preference is represented by a scale, for example, one that ranges from one to five ...
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Improved Collaborative Filtering

2011
We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly.
Aviv Nisgav, Boaz Patt-Shamir
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Discrete Collaborative Filtering

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete ...
Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, Tat-Seng Chua   +5 more
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Personalized collaborative filtering

Proceedings of the 29th Annual ACM Symposium on Applied Computing, 2014
In this paper, we propose a recommender system approach which considers contextual information from users and items in order to improve the accuracy of a neighborhood-based collaborative filtering algorithm. One advantage of our model is the possibility to bias the users' similarity computation according to a contextual constraint, such as the group of
Edson B. Santos, Rudinei Goularte, Marcelo G. Manzato   +2 more
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Nantonac collaborative filtering

Proceedings of the fourth ACM conference on Recommender systems, 2010
A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-scale, have been employed. We replaced such collection methods with a ranking method, in which objects are sorted according to the degree of a user's preference.
Toshihiro Kamishima, Shotaro Akaho
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Collaborative Filtering

2022
Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan   +3 more
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Collaborative Filtering System

2019 4th International Conference on Computer Science and Engineering (UBMK), 2019
Recommendation systems using machine learning created. Recommendation systems can be gathered around 3 main topics. Content-based, collaborative filtering and hybrid suggestion systems. Collaborative filtering system is a system where suggestions are made using similar users.
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Rethinking Collaborative Filtering

Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017
A decade has passed since the seminal Netflix Prize competition and Collaborative Filtering (CF) models are still at the forefront of Recommender System research. Significant progress has been achieved over this time, yet key aspects of the basic problem formulation have not been seriously challenged.
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Community Collaborative Filtering

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
This paper presents a novel approach from a perspective of considering community structures to collaborative filtering. In our approach, multiple types of information are be explored and exploited, including item content, user demography, use-item ratings, use-item structure and user social information.
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