Search

With the explosive increase in data on the web, recommending items to users is becoming more complex. In recent times, the best recommender systems have come from ensemble learning, which combines many models and techniques to generate recommendations that can draw the best characteristics of the constituent models. These ensemble models can improve accuracy, and they are also able to reduce the biases that come with each model. However, the massive number of permutations in which recommendation models are combined to create an ensemble model adds another layer of complexity to an already complex problem. Thus, in line with the recent trend in Automated Machine Learning (AutoML) aimed at reducing the complexity associated with model selection, there is a need for a machine learning framework that can learn the best ensemble model for a given problem given the base models. We proposed a system Ensemble with Particle Swarm Optimization, which intelligently optimizes the recommendations by identifying the best ensemble architecture for the data at hand. Our proposed AutoML system can improve recommendations for the MovieLens dataset by combining the results from base techniques without any user effort. The major challenge in creating such a system is to develop a framework for generating ensemble models and finding an efficient way to reach the best performing model since the search space can be vast. To overcome this challenge, we have used hierarchical models to generate ensembles and Particle Swarm Optimization to find the optimal ensemble models. [ABSTRACT FROM AUTHOR]

Recommender systems are becoming more essential than ever as the data available online is increasing manifold. The increasing data presents us with an opportunity to build complex systems that can model the user interactions more accurately and extract sophisticated features to provide recommendations with better accuracy. To construct these complex models, deep learning is emerging as one of the most powerful tools. It can process large amounts of data to learn the structure and patterns that can be exploited. It has been used in recommender systems to solve cold-start problem, better estimate the interaction functions, and extract deep feature representations, among other facets that plague the traditional recommender systems. As big data is becoming more prevalent, there is a need to use tools that can take advantage of such explosive data. An extensive study on recommender systems using deep learning has been performed in the paper. The literature review spans in-depth analysis and comparative study of the research domain. The paper exhibits a vast range of scope for efficient recommender systems in future. [ABSTRACT FROM AUTHOR]