A Semantic Approach for Fashion Recommendation Using Logistic Regression and Ontologies

Due to the increased prevalence of web recommendation systems after years of research, it has unarguably become the ultimate solution for efficient functioning of any e-commerce or user supportive digital domain. Though a variety of algorithms have been tested to meet the expectations of users in order to be decision supportive, this paper proposes a potential framework for recommendation of men's clothing. The focus of the system is to improve the efficiency of the recommendation to cope up to the speed of the user's thought process and expectations at the same time generate only those options that have been validated closely to the user's style hunt trajectory. In the presented approach the user's historical click data and searches is preprocessed and converted into query words. The features are extracted from the on ontology of fashion with the help of query words. The ontology used in this paper is highly domain specific. External sources such as fashion reviews, fashion e-magazines, fashion blogs and fashion trends from e-commerce websites are converted into query words and used for feature enrichment. The dataset is provided for classification using logistic regression, and only the top 50% of results from the classification undergoes semantic similarity computation. Normalized google distance and SemantoSim measure are the methods used for emantic similarity computation, this happens mainly for the relevance of the results. The recommendations of fashion items and fashion brands are suggested to the user based on the results gotten from semantic similarity. The accuracy of the Onto infused recommendation system is 97.14% and average precision is 96.31%.