Developing Hybrid-Based Recommender System with Naïve Bayes Optimization to Increase Prediction Efficiency

Commerce and entertainment world today have shifted to the digital platforms where customer preferences are suggested by recommender systems. Recommendations have been made using a variety of methods such as content-based, collaborative filtering-based or their hybrids. Collaborative systems are common recommenders, which use similar users’ preferences. They however have issues such as data sparsity, cold start problem and lack of scalability. When a small percentage of users express their preferences, data becomes highly sparse, thus affecting quality of recommendations. New users or items with no preferences, forms cold start issues affecting recommendations. High amount of sparse data affects how the user-item matrices are formed thus affecting the overall recommendation results. How to handle data input in the recommender engine while reducing data sparsity and increase its potential to scale up is proposed. This paper proposed development of hybrid model with data optimization using a Naïve Bayes classifier, with an aim of reducing data sparsity problem and a blend of collaborative filtering model and association rule mining-based ensembles, for recommending items with an aim of improving their predictions. Machine learning using python on Jupyter notebook was used to develop the hybrid. The models were tested using MovieLens 100k and 1M datasets. We demonstrate the final recommendations of the hybrid having new top ten highly rated movies with 68% approved recommendations. We confirm new items suggested to the active user(s) while less sparse data was input and an improved scaling up of collaborative filtering model, thus improving model efficacy and better predictions

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines.  In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset.  The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences

Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems

Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines.  In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset.  The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences