Leveraging Metropolis-Hastings Algorithm on Graph-based Model for Multimodal IR
نویسندگان
چکیده
The velocity of multimodal information shared on web has increased significantly. Many reranking approaches try to improve the performance of multimodal retrieval, however not in the direction of true relevancy of a multimodal object. Metropolis-Hastings (MH) is a method based on Monte Carlo Markov Chain (MCMC) for sampling from a distribution when traditional sampling methods such as transformation or inversion fail. If we assume this probability distribution as true relevancy of documents for an information need, in this paper we explore how leveraging our model with Metropolis-Hastings algorithm may help towards true relevancy in multimodal IR.
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