This work reports on the applicability of the Broadway recommendation computation approach for implementing a query refinement (QR) service in the context of a distributed information retrieval and gathering system. The Broadway recommendation approach is based on the following hypothesis: recommend to one user items or actions that have satisfied users that have behaved similarly to her/him. The case-based reasoning (CBR) paradigm is proposed to design Broadway-based recommenders. The design of such recommenders is facilitated by the use of the indexing scheme with time-extended situation of CBR*Tools, a CBR framework developped at INRIA; a crucial feature for enabling modeling and comparing users behaviors. We show how to integrate a query refinement recommender in the context CBKB, a meta-search engine developped at XRCE.
Applying the Broadway Recommendation Approach to Query Refinement in the CBKB MetaSearch Engine
| Jean-Marc Andreoli, Rushed Kanawati, Brigitte Trousse |
| Proc. of RAPC'99, Palaiseau, France, 15-17 June 1999 |
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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