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609
Context-aware recommender systems.
- In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys ’08,
, 2008
"... Abstract This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multi-criteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recomm ..."
Abstract
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Cited by 162 (29 self)
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-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders -techniques that provide recommendations by modelling a user's utility for an item as a vector of ratings along several criteria. A review of current algorithms that use multicriteria ratings for calculating predictions
Activating the Crowd: Exploiting User-Item Reciprocity for Recommendation
"... ABSTRACT Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowdsourcing, specifically, outsourcing micro-requests for ..."
Abstract
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Cited by 1 (0 self)
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ABSTRACT Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowdsourcing, specifically, outsourcing micro
User-Item Reciprocity in Recommender Systems: Incentivizing the Crowd
"... Abstract. Data consumption has changed significantly in the last 10 years. The digital revolution and the Internet has brought an abundance of information to users. Recommender systems are a popular means of finding content that is both relevant and personalized. However, today’s users require bette ..."
Abstract
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Cited by 1 (1 self)
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better recommender systems, able of producing continuous data feeds keeping up with their instantaneous and mobile needs. The CrowdRec project addresses this demand by providing context-aware, resource-combining, socially-informed, interactive and scalable recom-mendations. The key insight of Crowd
Learning Collaborative Information Filters
- In Proc. 15th International Conf. on Machine Learning
, 1998
"... Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from the ..."
Abstract
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Cited by 354 (4 self)
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Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from
Content-Based Book Recommending Using Learning for Text Categorization
- IN PROCEEDINGS OF THE FIFTH ACM CONFERENCE ON DIGITAL LIBRARIES
, 1999
"... Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. ..."
Abstract
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Cited by 334 (8 self)
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. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
- In Proceedings of the fourth ACM conference on Recommender systems
, 2010
"... Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we intro ..."
Abstract
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Cited by 77 (4 self)
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introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix
Collaborative Context-aware Preference Learning
"... Preference learning methods work by exploiting patterns in the data that relate users to items. Preference data often includes information such as the context of a recommendation (e.g. time/date, location). Leveraging this data (e.g. click logs, purchase/usage data) can significantly improve the rel ..."
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Preference learning methods work by exploiting patterns in the data that relate users to items. Preference data often includes information such as the context of a recommendation (e.g. time/date, location). Leveraging this data (e.g. click logs, purchase/usage data) can significantly improve
Incarmusic: Context-aware music recommendations
- in acar.InE-Commerce and Web Technologies - 12th International Conference, EC-Web 2011
"... Abstract. Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the ..."
Abstract
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Cited by 21 (8 self)
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Abstract. Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires
Context-aware movie recommendation based on signal processing and machine learning
- In Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRa ’11
, 2011
"... Most of the existing recommendation engines do not take into consideration contextual information for suggesting in-teresting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware ap-p ..."
Abstract
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Cited by 6 (1 self)
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Most of the existing recommendation engines do not take into consideration contextual information for suggesting in-teresting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware ap
Context-aware Splitting Approaches: Split Users or Split Items?
"... Abstract. Context-aware recommendation turns out to be popular in recent years – the end users can get a list of recommendations adapting to their contextual situations, where the context-aware splitting approach is one of the most popu-lar and efficient recommendation algorithms. There are three ty ..."
Abstract
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Abstract. Context-aware recommendation turns out to be popular in recent years – the end users can get a list of recommendations adapting to their contextual situations, where the context-aware splitting approach is one of the most popu-lar and efficient recommendation algorithms. There are three
Results 1 - 10
of
609
