18,405 research outputs found
TransNets: Learning to Transform for Recommendation
Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho
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