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A Sequential Recommendation Approach for Interactive Personalized Story Generation

A Sequential Recommendation Approach for Interactive Personalized Story Generation. By Hong Yu and Mark O. Riedl. . Overview. Drama Manager Player Modeling Filtering Architechure / Building a tree Test and Results. Drama Manager.

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A Sequential Recommendation Approach for Interactive Personalized Story Generation

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  1. A Sequential Recommendation Approach for InteractivePersonalized Story Generation By Hong Yu and Mark O. Riedl. 

  2. Overview • Drama Manager • Player Modeling • Filtering • Architechure / Building a tree • Test and Results

  3. Drama Manager • An agent used to 'bring about a particular sequence of plot points for the user to experience‘ • Mostly used to select plot points from a list that a designer has specified as a good story experience • Lacking is the players preferences, this is what this paper tries to find a solution to.

  4. Player Modeling • (-)Widely used well-defined types (Robins Laws) • Instead make types ‘on the fly’, based on user ratings • ” The basic assumption of our player modeling algorithms is that those people who share similar preference in the past tend to share it again in the future, hence we use a form of collaborative filtering.”

  5. Collaborative Filtering • Collaborative Filtering vs Prefixed Based CF • CF: A technique that uses patterns to find similarities between users and use this to recommend stuff • PBCF: Learns players preferences over fragments of story and applies it to the rest of the story.

  6. User Ratings and Filtering • Players rate ‘the story so far’ • Players that share preference in past ‘tend’ to share them in the future. (CF) • Construct a matrix of ratings

  7. The architecture

  8. Branching Story Graph • – a graph of plots with connections • - prefix graph (tree)

  9. Model Training • Build the story lib (store prefix forrest) • Data collection, populate prefix rating matrix • Player Model Parameters

  10. Story Generation • Model user preferences • Calculate ratings vector • Select highest rated full length story (decendant of current node) • Collect ratings of the story so far.

  11. Results • Test based on Choose-your-own-adventure • 5 rated sample stories = 80% prefer DM story to random • Drawback: Must collect ratings (when playing a game/reading a story/watching a movie, dont you love being asked to fill out ratings?)

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