What big brands know goal setting techniquesGerald O'Brion
Gerry O'Brion is a marketing speaker who helps companies grow by using strategies from billion dollar brands. His goal setting presentation ignites goals in organizations by involving the audience in a full goal setting exercise.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
What big brands know goal setting techniquesGerald O'Brion
Gerry O'Brion is a marketing speaker who helps companies grow by using strategies from billion dollar brands. His goal setting presentation ignites goals in organizations by involving the audience in a full goal setting exercise.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach — differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
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