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.
The growth of technology has seen development of smart devices that are connected to each other giving rise to device-mesh technology. This has given rise to many owners of these devices sharing data through various web applications such as online marketplaces. The protection of data is paramount for every organization dealing with such data. An evaluation of Blockchain technology as a solution to data privacy is studied. The study concludes that though blockchain is the technology to pursue for securing and protection data, it has numerous challenges and limitations towards data privacy. More research is needed to guarantee an absolute data privacy protection.
The world today is on revolution 4.0 which is data-driven. The majority of organizations and systems are using data to solve problems through use of digitized systems. Data lets intelligent systems and their applications learn and adapt to mined insights without been programmed. Data mining and analysis requires smart tools, techniques and methods with capability of extracting useful patterns, trends and knowledge, which can be used as business intelligence by organizations as they map their strategic plans. Predictive intelligent systems can be very useful in various fields as solutions to many existential issues. Accurate output from such predictive intelligent systems can only be ascertained by having well prepared data that suits the predictive machine learning function. Machine learning models learns from data input using the ‘garbage-in-garbage-out’ concept. Cleaned, pre-processed and consistent data would produce accurate output as compared to inconsistent, noisy and erroneous data.
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