However, the sparsity and shortness of essays will restrict the accuracy of text classification. With the continuous development of the Internet, social media based on short text has become popular. The outcomes of this work have implications for exploitation in recommender systems. Finally, we demonstrate that our proposed approach is able to predict the EO and HO of users from traces of interactions with movies substantially better than the baseline approaches. We performed a comparison of various predictive algorithms, as both regression and classification problems. We collected a dataset of 350 users, 703 movies and 3499 ratings. Our research goal is to devise a model that predicts the EH and HO of users from interaction data with movies, such as ratings. While the former accounts for how much a user is interested in content that deals with meaningful topics, the latter accounts for how much a user is interested in the entertaining quality of the content. The model is composed of two dimensions: the (i) eudaimonic orientation of users (EO) and (ii) hedonic orientation of users (HO). In this work, we focus on a personality model that is targeted at motivations for multimedia consumption. Personality, especially in the form of the Five Factor Model, has shown usefulness in personalized systems, such as recommender systems. Personality accounts for how individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles.
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