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      A Knowledge-based Filtering Story Recommender System for Theme Lovers with an Application to the Star Trek Television Franchise

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          Abstract

          In this paper, we propose a recommender system that takes a user-selected story as input and returns a ranked list of similar stories on the basis of shared literary themes. The user of our system first selects a story of interest from a list of background stories, and then sets, as desired, a handful of knowledge-based filtering options, including the similarity measure used to quantify the similarity between story pairs. As a proof of concept, we validate experimentally our system on a dataset comprising 452 manually themed Star Trek television franchise episodes by using a benchmark of curated sets of related stories. We show that our manual approach to theme assignment significantly outperforms an automated approach to theme identification based on the application of topic models to episode transcripts. Additionally, we compare different approaches based on sets and on a hierarchical-semantic organization of themes to construct similarity functions between stories. The recommender system is implemented in the R package stoRy. A related R Shiny web application is available publicly along with the Stark Trek dataset including the theme ontology, episode annotations, storyset benchmarks, transcripts, and evaluation setup.

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          Zoogeographical Studies on the Soleoid Fishes Found in Japan and its Neighhouring Regions-II

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            Mathematical properties of soft cardinality: Enhancing Jaccard, Dice and cosine similarity measures with element-wise distance

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              Text Comparison Using Soft Cardinality

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                Author and article information

                Journal
                31 July 2018
                Article
                1808.00103
                2027ec0b-8e59-471c-8b05-f90d521011d0

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                18 pages, 4 figures, 5 tables, 1 supplementary material
                cs.IR cs.CL

                Theoretical computer science,Information & Library science
                Theoretical computer science, Information & Library science

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