| Summary: | 碩士 === 國立成功大學 === 資訊工程學系 === 107 === 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.
|
|---|
