2 research outputs found
Latent Multi-Criteria Ratings for Recommendations
Multi-criteria recommender systems have been increasingly valuable for
helping consumers identify the most relevant items based on different
dimensions of user experiences. However, previously proposed multi-criteria
models did not take into account latent embeddings generated from user reviews,
which capture latent semantic relations between users and items. To address
these concerns, we utilize variational autoencoders to map user reviews into
latent embeddings, which are subsequently compressed into low-dimensional
discrete vectors. The resulting compressed vectors constitute latent
multi-criteria ratings that we use for the recommendation purposes via standard
multi-criteria recommendation methods. We show that the proposed latent
multi-criteria rating approach outperforms several baselines significantly and
consistently across different datasets and performance evaluation measures.Comment: Accepted to RecSys19
Latent Unexpected Recommendations
Unexpected recommender system constitutes an important tool to tackle the
problem of filter bubbles and user boredom, which aims at providing unexpected
and satisfying recommendations to target users at the same time. Previous
unexpected recommendation methods only focus on the straightforward relations
between current recommendations and user expectations by modeling
unexpectedness in the feature space, thus resulting in the loss of accuracy
measures in order to improve unexpectedness performance. Contrast to these
prior models, we propose to model unexpectedness in the latent space of user
and item embeddings, which allows to capture hidden and complex relations
between new recommendations and historic purchases. In addition, we develop a
novel Latent Closure (LC) method to construct hybrid utility function and
provide unexpected recommendations based on the proposed model. Extensive
experiments on three real-world datasets illustrate superiority of our proposed
approach over the state-of-the-art unexpected recommendation models, which
leads to significant increase in unexpectedness measure without sacrificing any
accuracy metric under all experimental settings in this paper.Comment: Accepted at ACM TIS