Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model

doi:10.23940/ijpe.22.08.p3.552558

Abstract

Abstract: Instagram is one of the leading social media networks with millions of users accessing this platform on a daily basis. From a business point of view, it is a billion-dollar industry, especially with paid promotional posts. The visibility of an Instagram post is dependent on the user’s social ties to the platform and the hashtags associated with the post. In this paper, we have proposed a category/genre-based hashtag recommendation system for Instagram posts. The proposed system uses pre-trained EfficientNet trained on “imagenet” weights for deep feature extraction and an Applied Least Squares (ALS) model for generating hashtags. Transfer learning is used with the base model of pre-trained EfficientNetB2 to enhance the feature extraction process. We further compare the performance of our model with similar recommendation models by replacing the EfficeintNetB2 with other popular CNNs (Resnet-50, Resnet-169, and Inceptionnet-V4). Our proposed model with EfficeintNetB2 achieves better precision, recall, and F1-measure values compared to other similar models using CNNs as feature extractors.

Key words: applied least square, efficientNetB2, convolution neural network, hashtag recommendation, feature extraction

Sagnik Pal, Rutvik Patel, Vijayasherly V., and Ramani Selvanambi. Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model [J]. Int J Performability Eng, 2022, 18(8): 552-558.

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