A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis
In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different resources have been collected throughout the years to describe it, a single database combining all these data sources is a valuable asset for cross-disciplinary investigations. We propose an integrated graph-based structure of multiple resources, enriched with the results of the application of graph analytics approaches to provide an encompassing view of the domain and of the way people talk about it during the recommendation task. While we cannot distribute the final resource because of licensing issues, we share the code to assemble and process it once the reference data have been obtained from the original sources.
Code (0)
등록된 구현이 없습니다.
Tasks
Movie RecommendationSimilar Papers 제목 키워드 기반
DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph
Summarizing movie screenplays presents a unique set of challenges compared to standard document summarization. Screenplays are not only lengthy, but also feature a complex interplay of characters, dialogues, and scenes, …
Document SummarizationQuestion AnsweringDOCENT: Learning Self-Supervised Entity Representations from Large Document Collections
This paper explores learning rich self-supervised entity representations from large amounts of the associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retriev…
Knowledge Base CompletionNatural Language QueriesQuestion AnsweringRetrieval+2Knowledge-aware attentional neural network for review-based movie recommendation with explanations
In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities from movie reviews and capturing understandable interactions bet…
Movie RecommendationRecommendation SystemsWord EmbeddingsMoviescope: Large-scale Analysis of Movies using Multiple Modalities
Film media is a rich form of artistic expression. Unlike photography, and short videos, movies contain a storyline that is deliberately complex and intricate in order to engage its audience. In this paper we present a la…
Transformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to …
Collaborative FilteringContrastive LearningRecommendation Systems