346 research outputs found
Causal Disentangled Recommendation Against User Preference Shifts
Recommender systems easily face the issue of user preference shifts. User
representations will become out-of-date and lead to inappropriate
recommendations if user preference has shifted over time. To solve the issue,
existing work focuses on learning robust representations or predicting the
shifting pattern. There lacks a comprehensive view to discover the underlying
reasons for user preference shifts. To understand the preference shift, we
abstract a causal graph to describe the generation procedure of user
interaction sequences. Assuming user preference is stable within a short
period, we abstract the interaction sequence as a set of chronological
environments. From the causal graph, we find that the changes of some
unobserved factors (e.g., becoming pregnant) cause preference shifts between
environments. Besides, the fine-grained user preference over categories
sparsely affects the interactions with different items. Inspired by the causal
graph, our key considerations to handle preference shifts lie in modeling the
interaction generation procedure by: 1) capturing the preference shifts across
environments for accurate preference prediction, and 2) disentangling the
sparse influence from user preference to interactions for accurate effect
estimation of preference. To this end, we propose a Causal Disentangled
Recommendation (CDR) framework, which captures preference shifts via a temporal
variational autoencoder and learns the sparse influence from multiple
environments. Specifically, an encoder is adopted to infer the unobserved
factors from user interactions while a decoder is to model the interaction
generation process. Besides, we introduce two learnable matrices to disentangle
the sparse influence from user preference to interactions. Lastly, we devise a
multi-objective loss to optimize CDR. Extensive experiments on three datasets
show the superiority of CDR.Comment: This paper has been accepted for publication in Transactions on
Information System
Controllable Recommenders using Deep Generative Models and Disentanglement
In this paper, we consider controllability as a means to satisfy dynamic
preferences of users, enabling them to control recommendations such that their
current preference is met. While deep models have shown improved performance
for collaborative filtering, they are generally not amenable to fine grained
control by a user, leading to the development of methods like deep language
critiquing. We propose an alternate view, where instead of keyphrase based
critiques, a user is provided 'knobs' in a disentangled latent space, with each
knob corresponding to an item aspect. Disentanglement here refers to a latent
space where generative factors (here, a preference towards an item category
like genre) are captured independently in their respective dimensions, thereby
enabling predictable manipulations, otherwise not possible in an entangled
space. We propose using a (semi-)supervised disentanglement objective for this
purpose, as well as multiple metrics to evaluate the controllability and the
degree of personalization of controlled recommendations. We show that by
updating the disentangled latent space based on user feedback, and by
exploiting the generative nature of the recommender, controlled and
personalized recommendations can be produced. Through experiments on two widely
used collaborative filtering datasets, we demonstrate that a controllable
recommender can be trained with a slight reduction in recommender performance,
provided enough supervision is provided. The recommendations produced by these
models appear to both conform to a user's current preference and remain
personalized.Comment: 10 pages, 1 figur
Aprendizaje de representaciones desenredadas de escenas a partir de imágenes.
Artificial intelligence is at the forefront of a technological revolution, in particular as a key component to build autonomous agents. However, not only training such agents come at a great computational cost, but they also end up lacking human basic abilities like generalization, information extrapolation, knowledge transfer between contexts, or improvisation. To overcome current limitations, agents need a deeper understanding of their environment, and more efficiently learning it from data. There are very recent works that propose novel approaches to learn representations of the world: instead of learning invariant object encodings, they learn to isolate, or disentangle, the different variable properties which form an object. This would not only enable agents to understand object changes as modifications of one of their properties, but also to transfer such knowledge on the properties between different categories. This Master Thesis aims to develop a new machine learning model for disentangling object properties on monocular images of scenes. Our model is based on a state-of-the-art architecture for disentangled representations learning, and our goal is to reduce the computational complexity of the base model while also improving its performance. To achieve this, we will replace a recursive unsupervised segmentation network by an encoder-decoder segmentation network. Furthermore, before training such overparametrized neural model without supervision, we will profit from transfer learning of pre-trained weights from a supervised segmentation task. After developing a first vanilla model, we have tuned it to improve its performance and generalization capability. Then, an experimental validation has been performed on two commonly used synthetic datasets, evaluating both its disentanglement performance and computational efficiency, and on a more realistic dataset to analyze the model capability on real data. The results show that our model outperforms the state of the art, while reducing its computational footprint. Nevertheless, further research is needed to bridge the gap with real world applications.<br /
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