Directional Multivariate Ranking - CrossMinds.ai
Directional Multivariate Ranking
Aug 13, 20207 views
Nan Wang
User-provided multi-aspect evaluations manifest users’ detailed,feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that,modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in,recommendation, there is no principled solution yet for collectively,generating multiple item rankings over different aspects.,In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple,aspects. Specifically, we view multi-aspect evaluation as an integral,effort from a user that forms a vector of his/her preferences over,aspects. Our key insight is that the direction of the difference vector,between two multi-aspect preference vectors reveals the pairwise,order of comparison. Hence, it is necessary for a multi-aspect ranking criterion to preserve the observed directions from such pairwise,comparisons. We further derive a complete solution for the multiaspect ranking problem based on a probabilistic multivariate tensor,factorization model. Comprehensive experimental analysis on a,large TripAdvisor multi-aspect rating dataset and a Yelp review text,dataset confirms the effectiveness of our solution.
SIGKDD_2020
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