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- W2893250079 abstract "The proposed work aims to recommend relevant hash-tags from social media posts and blog posts related to food, recipes and other popular domains using topic word-embeddings. These hash-tags can be used in turn to give food item/recipe as the user requires. For topic modeling a machine learning approach called LDA(Latent Dirichlet Allocation) is used. The method treats the social media posts as a corpus of words and applies a topic modeling to generate the relevant hash-tags. The hash-tags recommended are cross validated with a word embedding model developed from the Wikipedia corpus to check the accuracy of the recommendation system. This algorithm is further tested on posts that do not contain hash-tags to recommend new relevant hash-tags for the same." @default.
- W2893250079 created "2018-10-05" @default.
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- W2893250079 date "2018-04-01" @default.
- W2893250079 modified "2023-09-27" @default.
- W2893250079 title "A Word Embedding Model for Topic Recommendation" @default.
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- W2893250079 doi "https://doi.org/10.1109/icicct.2018.8473055" @default.
- W2893250079 hasPublicationYear "2018" @default.
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