A Word Embedding Model for Topic Recommendation
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.
2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)