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ECIR
2006
Springer

A User-Item Relevance Model for Log-Based Collaborative Filtering

10 years 27 days ago
A User-Item Relevance Model for Log-Based Collaborative Filtering
Abstract. Implicit acquisition of user preferences makes log-based collaborative filtering favorable in practice to accomplish recommendations. In this paper, we follow a formal approach in text retrieval to re-formulate the problem. Based on the classic probability ranking principle, we propose a probabilistic user-item relevance model. Under this formal model, we show that user-based and item-based approaches are only two different factorizations with different independence assumptions. Moreover, we show that smoothing is an important aspect to estimate the parameters of the models due to data sparsity. By adding linear interpolation smoothing, the proposed model gives a probabilistic justification of using TF
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ECIR
Authors Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
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