Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data. There are different scenarios where meta-learning can be applied, and one of the most common is algorithm recommendation, where previous experience on applying machine learning algorithms for several datasets can be used to learn which algorithm, from a set of options, would be more suitable for a new dataset. Perhaps the most popular form of meta-learning is transfer learning, which consists of transferring knowledge acquired by a machine learning algorithm in a previous learning task to increase its performance faster in another and similar task. Transfer Learning has been widely applied in a variety of complex tasks such as image classification, machine translation and speech recognition, achieving remarkable results. Although transfer learning is very used in traditional or base-learning, it is still unknown if it is useful in a meta-learning setup. For that purpose, in this paper, we investigate the effects of transferring knowledge in the meta-level instead of base-level. Thus, we train a neural network on meta-datasets related to algorithm recommendation, and then using transfer learning, we reuse the knowledge learned by the neural network in other similar datasets from the same domain, to verify how transferable is the acquired meta-knowledge.
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The recommendation task is a prominent and challenging area of study in Machine Learning. It aims to recommend items such as products, movies, and services to users according to what they have liked in the past. In general, most of the recommendation systems only consider structured information. For instance, in recommending movies to users they might use features such as genre, actors, and directors. However, unstructured data such as users' reviews may also be considered, since they can aggregate important information to the recommendation process, improving the performance of recommendation systems. Thus, in this work, we evaluate the use of text mining methods to extract and represent relevant information about user reviews, which were used alongside with rating data, as input of a content-based recommendation algorithm. We considered three different strategies for this purpose, which were: Topics, Embeddings and Relevant Embeddings. We hypothesized that using the considered strategies, it is possible to create more meaningful and concise representations than the traditional bag-of-words model, and yet, increase the performance of recommendation systems. In our experimental evaluation, we confirmed such a hypothesis, showing that the considered representations strategies are indeed very promising for representing user reviews in the recommendation process.
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