Latent Collaborative Retrieval
, , , и .
(2012)cite arxiv:1206.4603Comment: ICML2012.

Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.
URL
http://arxiv.org/abs/1206.4603
искать в
  • @hotho
К этой публикации ещё не было создано рецензий.

распределение оценок
средняя оценка пользователей0,0 из 5.0 на основе 0 рецензий
    Пожалуйста, войдите в систему, чтобы принять участие в дискуссии (добавить собственные рецензию, или комментарий)