Abstract. The Mongue-Elkan method is a general text string comparison method based on an internal character-based similarity measure (e.g. edit distance) combined with a token level (i.e. word level) similarity measure. We propose a generalization of this method based on the notion of the generalized arithmetic mean instead of the simple average used in the expression to calculate the Monge-Elkan method. The experiments carried out with 12 well-known name-matching data sets show that the proposed approach outperforms the original Monge-Elkan method when character-based measures are used to compare tokens.

Collaborative filtering based recommender systems have been extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain when items mostly belong to the same category and are mostly alike (e.g. television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN algorithm. As study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We observed that our proposed method showed to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Recommender systems allow the exploration of large collections of products, the discovery of patterns in the products, and the guidance of users towards products that match their interests. Collaborative tagging systems allow users to label products in a collection using a free vocabulary. The aggregation of these tags, also called a Folksonomy, can be used to build a collective characterization of the products in a simple and recognizable vocabulary. In this paper, we propose a family of methods called LinearTag recommenders, which infer users preferences for tags to formulate recommendations for them. We dubbed these inferred user profiles as TagProfiles. We present experiments using them as an interaction artifact that allows users to receive new recommendations as they delete, add or reorder tags in their profiles. Additional experiments using the Movielens dataset, show that the proposed methods generate recommendations with an error margin similar, or even lower than the results reported by methods based on latent factors. Next, we compared TagProfiles against KeywordProfiles, which are profiles based on keywords extracted automatically from textual descriptions of products. This comparison showed that TagProfiles are not only more precise in their predictions, but they are also more understandable by users. At last, we developed a user interface of a movie recommender based on TagProfiles, which we tested with 25 users. This experience showed that TagProfiles are easier to understand and modify by users, allowing them to discover new movies as they interact with their profiles.

Background Even though the importance of preparing patients for a surgical event is recognized, there are still gaps about the benefit of improving functional capacity by walking during the waiting time among patients scheduled for non-cardiac surgery. The aim of this study was to evaluate the impact of pre-surgical walking in-hospital length of stay, early ambulation, and the appearance of complications after surgery among patients scheduled for non-cardiac surgery. Methods A two-arm, single- blinded randomized controlled trial was developed from May 2016 to August 2017. Eligible outpatients scheduled for non-cardiac surgery, capable of walking, were randomized (2:1 ratio) to receive a prescription of walking 150 min/week during the whole pre-surgical waiting time (n = 249) or conventional care (n = 119). The primary outcome was the difference in hospital length of stay, and secondary results were time to first ambulation during hospitalization, description of ischemic events during hospitalization and after six months of hospital discharge, and the walking continuation. We performed an intention to treat analysis and compared length of stay between both groups by Kaplan–Meier estimator (log-rank test). Results There were no significant differences in the length of hospital stay between both groups (log-rank test p = 0.367) and no differences in the first ambulation time during hospitalization (log-rank test p = 0.299). Similar rates of postoperative complications were observed in both groups, but patients in the intervention group continued to practice walking six months after discharge (p < 0.001). Conclusion Our study is the first clinical trial evaluating the impact of walking before non-cardiac surgery in the length of stay, early ambulation, and complications after surgery. Prescription of walking for patients before non-cardiac surgery had no significant effect in reducing the length of stay, and early ambulation. The results become a crucial element for further investigation. Trial registration: PAMP-Phase2 was registered in ClinicalTrials.gov NCT03213496 on July 11, 2017.

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