Improving Multidimensional Recommender Systems Using Dimensions as Virtual Items

• Marcos Domingues USP
• Alípio Jorge Universidade do Porto
• Carlos Soares Universidade do Porto
• Solange Rezende USP

Resumo

The first multidimensional algorithm for recommender systems is the well known combined reduction-based, which treats additional dimensions as labels for segmenting/filtering sessions, using the segmented sessions to build the recommendation model. This algorithm only uses the additional dimensions when it outperforms the traditional two-dimensional algorithm. Otherwise, it reverts to the traditional two-dimensional algorithm to generate the top-N recommendations. In this paper, we propose to improve the combined reduction-based algorithm by using the DaVI approach, which handles additional dimensions as virtual items. Incorporating the DaVI approach into the combined reductionbased, the multidimensional algorithm uses the additional dimensions not only as labels for segmenting sessions but also as virtual items to improve the recommendation model. The empirical results demonstrate that our proposal reduces the needs of reverting to the traditional two-dimensional algorithm to generate the top-N recommendations, increasing the performance of the combined reduction-based algorithm.

Referências

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