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• W2169583208 abstract "Recommender systems support online customers by suggesting products and services of likely interest to them. Taking into consideration the context of customers is believed to produce better recommendations, yet it poses unique challenges. If a recommendation is generated through previous ratings, narrowing down the set of ratings to those under the target context will limit the number, producing poor-quality recommendations. A common approach to improving the quality of recommendations is to aggregate ratings from a number of similar context segments; however, establishing which segments to aggregate by unguided enumerations is too computationally intensive. In this paper, we propose a novel context similarity metric to guide the aggregation process and show how it can be extended across multiple context dimensions. The metric underpins another contribution: a guided aggregation approach to context-based recommendation. This approach can be combined with traditional recommendation algorithms to improve th..." @default.
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• W2169583208 date "2013-10-01" @default.
• W2169583208 modified "2023-10-16" @default.
• W2169583208 title "Context Similarity Metric for Multidimensional Service Recommendation" @default.
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• W2169583208 doi "https://doi.org/10.2753/jec1086-4415180103" @default.
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