Learning Representations of Hierarchical Slates in Collaborative Filtering

• URL: http://arxiv.org/abs/2010.06987v1
• Date: Fri, 25 Sep 2020 18:34:02 GMT
• Title: Learning Representations of Hierarchical Slates in Collaborative Filtering
• Authors: Ehtsham Elahi and Ashok Chandrashekar
• Abstract summary: We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data.
• Score: 2.8145809047875066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data. Our representation learning algorithm can be viewed as a simple composition rule that can be applied recursively in a bottom-up fashion to represent arbitrarily complex hierarchical structures in terms of the representations of its constituent components. We demonstrate our ideas on two real world recommendation systems datasets including the one used for the RecSys 2019 challenge. For that dataset, we improve upon the performance achieved by the winning team's model by incorporating embeddings as features generated by our approach in their solution.

Related papers

• Learning Visual Hierarchies with Hyperbolic Embeddings [28.35250955426006]
  We introduce a learning paradigm that can encode user-defined multi-level visual hierarchies in hyperbolic space without requiring explicit hierarchical labels. We show significant improvements in hierarchical retrieval tasks, demonstrating the capability of our model in capturing visual hierarchies.
  arXiv  Detail & Related papers  (2024-11-26T14:58:06Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• From Logits to Hierarchies: Hierarchical Clustering made Simple [16.132657141993548]
  We show that a lightweight procedure implemented on top of pre-trained non-hierarchical clustering models outperforms models designed specifically for hierarchical clustering. Our proposed approach is computationally efficient and applicable to any pre-trained clustering model that outputs logits, without requiring any fine-tuning.
  arXiv  Detail & Related papers  (2024-10-10T12:27:45Z)
• DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System [83.34921966305804]
  Large language models (LLMs) have demonstrated remarkable performance in recommender systems. We propose a novel plug-and-play alignment framework for LLMs and collaborative models. Our method is superior to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-08-15T15:56:23Z)
• Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation [48.08348593449897]
  We propose a simple yet effective personalized federated learning framework. We group clients into multiple clusters based on their model training status and data distribution on the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies.
  arXiv  Detail & Related papers  (2024-01-29T04:14:02Z)
• Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation [2.2265038612930663]
  This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks. We devise a training strategy that balances the contributions of features from multiple scales, ensuring that the learned representations capture both coarse and fine-grained details.
  arXiv  Detail & Related papers  (2024-01-12T09:47:17Z)
• Exploiting Data Hierarchy as a New Modality for Contrastive Learning [0.0]
  This work investigates how hierarchically structured data can help neural networks learn conceptual representations of cathedrals. The underlying WikiScenes dataset provides a spatially organized hierarchical structure of cathedral components. We propose a novel hierarchical contrastive training approach that leverages a triplet margin loss to represent the data's spatial hierarchy in the encoder's latent space.
  arXiv  Detail & Related papers  (2024-01-06T21:47:49Z)
• Learning Representations without Compositional Assumptions [79.12273403390311]
  We propose a data-driven approach that learns feature set dependencies by representing feature sets as graph nodes and their relationships as learnable edges. We also introduce LEGATO, a novel hierarchical graph autoencoder that learns a smaller, latent graph to aggregate information from multiple views dynamically.
  arXiv  Detail & Related papers  (2023-05-31T10:36:10Z)
• Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
  In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
  arXiv  Detail & Related papers  (2023-05-24T11:05:12Z)
• Multi-Task Off-Policy Learning from Bandit Feedback [54.96011624223482]
  We propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.
  arXiv  Detail & Related papers  (2022-12-09T08:26:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.