Abstract
Recommendation is very crucial technique for social networking sites and business organizations. It provides suggestions based on users’ personalized interest and provide users with movies, books and topics links that would be most suitable for them. It can improve user effectiveness and business revenue by approximately 30%, if analyzed in intelligent manner. Social recommendation systems for traditional datasets are already analyzed by researchers and practitioners in detail. Several researchers have improved recommendation accuracy and throughput by using various innovative approaches. Deep learning has been proven to provide significant improvements in image processing and object recognition. It is machine learning technique where hidden layers are used to improve outcome. In traditional recommendation techniques, sparsity and cold start are limitations which are due to less user-item interactions. This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications. Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques. When these recommendation systems are deployed on large scale of data, accuracy degrades significantly. Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) are used as evaluation parameters which are used to prove better recommendation accuracy. Epinions, MovieLens and FilmTrust datasets are also shown as most commonly used datasets for recommendation purpose.
Highlights
Social networking sites are used due to a lot of important information available
In this paper content based, collaborative filtering based and hybrid based techniques for recommendations are explained in detail
It was mentioned that sparsity, cold start and scalability are the issues in recommendation techniques
Summary
Social networking sites are used due to a lot of important information available. Large numbers of users interact with each other; share their views on these sites. Recommender systems are capable of providing better suggestions to users based on their past history, likings, ratings and trust amongst users. It is dependent on user behavior data and historical data [1]. Users likings is the main factor for recommendation. Ratings based recommendations are provided in collaborative filtering technique. Large scale graph partitioning, clustering, dimensionality reduction and deep learning are techniques which are provided by recent researches to improve recommendation. There are models which are beneficial in content based recommendation and others are applicable in collaborative filtering techniques.
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More From: International Journal of Advanced Computer Science and Applications
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