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Hybrid Recommender System: Link Analysis and Genetic Tuning on BoardGameGeek Data

This research explores a hybrid recommender system leveraging link analysis and genetic algorithm tuning applied to the bipartite network of BoardGameGeek.com. Utilizing a dataset of the top 5,000 games and users who have rated these games, we develop an innovative approach integrating matrix link analysis and content-based recommendations. The system aims to enhance predictive accuracy for user preferences, facilitated by optimal weight configurations determined through genetic tuning, addressing sparsity challenges in recommendation systems.

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Hybrid Recommender System: Link Analysis and Genetic Tuning on BoardGameGeek Data

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  1. A Hybrid Recommender System Using Link Analysis and Genetic Tuning in the Bipartite Network of BoardGameGeek.com Brett Boge CS 765 University of Nevada, Reno

  2. Data (Overview)

  3. Data (Scope) • Starting with the top 5,000 games • List of users == those which have rated at least one of the top 5,000 games • Users with no ratings cannot be connected to anycomponent of the graph, and can only be evaluatedin the most general sense

  4. Data (Retrieval) • Data will be obtained through the BGG XML API2 • Game|Small World, id 40692 • http://boardgamegeek.com/xmlapi2/thing?id=40692&ratingcomments=1 • User|Licinian • http://boardgamegeek.com/xmlapi2/user?name=Licinian • http://boardgamegeek.com/xmlapi2/ • collection?name=Licinian • &own/played/trade/want/wishlist/etc

  5. Data (Sets)

  6. General Approach

  7. General Approach Approaches R. Burke, "Hybrid recommender systems: Survey and experiments,"

  8. General Approach Approaches

  9. Link Analysis Step Approaches From Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

  10. ConsumerRepresentativeness • Matrix Link Analysis Step Approaches • ProductRepresentativeness • Matrix From Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

  11. ConsumerRepresentativeness • Matrix Link Analysis Step Approaches • ProductRepresentativeness • Matrix From Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

  12. ConsumerRepresentativeness • Matrix Link Analysis Step Approaches • ProductRepresentativeness • Matrix From Z. Huang, et al., "A Link analysis approach to recommendation under sparse data," 2004.

  13. Content-based Cascade Approaches • ProductRepresentativeness • Matrix PRi

  14. Content-based Cascade Approaches

  15. Content-based Cascade Approaches These will need to be normalized on the same scale (0.00 - 1.00)

  16. Content-based Cascade Approaches

  17. Content-based Cascade Approaches • Create PRfinal by refining PR: • W is a vector of weights which determine how much a givenproperty should effect the original score

  18. Genetic Tuning Approaches • W needs to be defined optimally for this given domain • A genetic algorithm will be used to tune W • Chromosome = sequential binary representation of W • Fitness based on Rank Score (from Huang et al.) • 8 bits per weight, ranging from 0 - .25 to start • Rates of crossover/mutation TBD

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