Abstract
The model proposes image recommendation method by generating tags which is the state of the art in the evolving web 3.0. Proposed model works on the principle of enrichment of queries through topic Modelling and standard knowledge repositories. Data set driven topic synthesis and metadata synthesis by classifying it using Bi-LSTM classifier is the basis for the model. Strong semantic similarity computation measures such as Piyanka index Lance and William index and adaptive pointwise mutual index measures are integrated into the model. An intermediate semantic network is formalized, and Optimization is achieved using the harmonic search algorithm. Proposed MLSM is best among the baseline models with Precision of 94.09% recall of 96.91%.
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Shrivastava, R.R., Deepak, G. (2024). MLSM: A Metadata Driven Learning Infused Semantics Oriented Model for Web Image Recommendation via Tags. In: Muthalagu, R., P S, T., Pawar, P.M., R, E., Prasad, N.R., Fiorentino, M. (eds) Computational Intelligence and Network Systems. CINS 2023. Communications in Computer and Information Science, vol 1978. Springer, Cham. https://doi.org/10.1007/978-3-031-48984-6_4
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DOI: https://doi.org/10.1007/978-3-031-48984-6_4
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