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Joint personalized search and recommendation with hypergraph convolutional networks
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| Thibaut Thonet, Jean-Michel Renders, Mario Choi, Jinho Kim |
| 44th European Conference on Information Retrieval (ECIR), Stavanger, Norway, 10-14 April, 2022 |
| Download |
| GitHub |
Traditionally, the search and recommendation tasks are performed separately, by distinct models. Having a unique model for the two tasks is however particularly appealing for platforms that offer search and recommendation services to a shared user base over common items. In this paper, we study this unification scenario denoted as Joint Personalized Search and Recommendation (JPSR). To tackle this problem, we introduce HyperSaR, an hypergraph convolutional approach for search and recommendation. From the interaction data, we first build an hypergraph composed of user, item and query keyword nodes in which recommendation instances form user-item edges and search instances define user-item-query hyperedges. We then propagate user, item and query keyword embeddings using hypergraph convolution, and train HyperSaR with the combination of two complementary losses. The first one amounts to assessing the probability of an interaction, while the second one aims at predicting the query of a search interaction given a (user, item) pair. The proposed method is evaluated on the JPSR task using three datasets: a real-world, industrial dataset, and the public MovieLens and Lastfm datasets, which have been adapted to the task. Our experiments demonstrate the superior effectiveness of HyperSaR over competing approaches.
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To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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