IJBTT

Text Based and Image Based Recommender Systems: Fundamental Concepts, Comprehensive Review and Future Directions

Text Based and Image Based Recommender Systems: Fundamental Concepts, Comprehensive Review and Future Directions
	© 2022 by IJETT Journal
	Volume-70 Issue-10
	Year of Publication : 2022
	Authors : Anu Mathews, N. Sejal, K. R. Venugopal
	DOI : 10.14445/22315381/IJETT-V70I10P214

How to Cite?

Anu Mathews, N. Sejal, K. R. Venugopal, "Text Based and Image Based Recommender Systems: Fundamental Concepts, Comprehensive Review and Future Directions," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 124-143, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P214

Abstract
The exponential growth of data on the Internet leads to the information overload problem, wherein users are presented with a huge mixture of irrelevant and relevant data, making their decision-making process complicated and time-consuming. Recommender Systems are software agents that learn the preferences of individual users and give recommendations accordingly. The availability of exploitable data, including implicit and explicit user feedback, decides these systems' performance. Machine learning algorithms have increased the efficiency of recommender systems by providing recommendations to users based on the users’ visual preferences. This paper reviews and classifies recommender systems based on their application domains and provides insights into the underlying concepts, including selecting features and algorithms under each classification. The challenges in developing recommender systems are discussed, considering which e-commerce marketplaces can be transformed to provide better customer satisfaction.

Keywords
Feature extraction, Similarity, Precision, recall, Prediction, Recommendation, Convolutional Neural Network (CNN), Deep learning.

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