PUMA publications for /author/Adam%20Berenzweighttps://puma.uni-kassel.de/author/Adam%20BerenzweigPUMA RSS feed for /author/Adam%20Berenzweig2024-03-29T06:09:47+01:00Latent Collaborative Retrievalhttps://puma.uni-kassel.de/bibtex/279c6771a9b032497635d5f39a39e921a/hothohotho2012-08-15T11:07:09+02:00recommender tensor toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jason Weston" itemprop="url" href="/author/Jason%20Weston"><span itemprop="name">J. Weston</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chong Wang" itemprop="url" href="/author/Chong%20Wang"><span itemprop="name">C. Wang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ron Weiss" itemprop="url" href="/author/Ron%20Weiss"><span itemprop="name">R. Weiss</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adam Berenzweig" itemprop="url" href="/author/Adam%20Berenzweig"><span itemprop="name">A. Berenzweig</span></a></span>. </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)<em>cite arxiv:1206.4603Comment: ICML2012.</em>Wed Aug 15 11:07:09 CEST 2012cite arxiv:1206.4603Comment: ICML2012Latent Collaborative Retrieval2012recommender tensor toread 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.Latent Collaborative Retrieval