Using Relationship and Scenario Features of Plot Summaries for Social Article Trailer Recommendation

Abstract

The post articles on the social platform is the favorite activity of young people With the potential of digital movie industry developing automatic movie recommendation engines becomes a popular issue On social media in the scenario of sharing related trailers with user-generated articles about daily life online social platforms users tend to choose trailers considering their lyrical theme To solve the above problem we present a Relationship-Scenario-based Trailer Recommendation System which can recommend list of trailers to an input article by analyzing lyrical theme We consider lyrical theme as a combination of Relationship and Scenario the subjective and objective perspective of plot summaries By utilizing relationship-scenario Database (Extended-HowNet as Knowledge base) we extract relationship and scenario features of plot summaries and articles Relationship feature is represented as character emotion event location and time entity relation And scenario feature is represented as emotion and event entity relation Consequently we show that using both relationship and scenario features provide better recommendation results than merely consider one of the features In the end our recommender system outperforms a novel W2V baseline in both experiments of user preference and system performance Also we consider user preference on our system about different relationship class

Date of Award	2019
Original language	English
Supervisor	Wen-Hsiang Lu (Supervisor)