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Grocery Shopping Recommendations Based on Basket-Sensitive Random Walk

Outline. IntroductionBackgroundItem-based Collaborative FilteringRandom Walk Model for Product RecommendationBasket-Sensitive Random Walk on Bipartite NetworkExperimentsConclusions and Future WorkMy thoughts. Introduction. Shopping websitesforgotten itemsnew but relevant productsBuy novel

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Grocery Shopping Recommendations Based on Basket-Sensitive Random Walk

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    1. Grocery Shopping Recommendations Based on Basket-Sensitive Random Walk KDD 2009 Ming Li, Benjamin Dias, Ian Jarman, Wael El-Deredy, Paulo J. G. Lisboa

    2. Outline Introduction Background Item-based Collaborative Filtering Random Walk Model for Product Recommendation Basket-Sensitive Random Walk on Bipartite Network Experiments Conclusions and Future Work My thoughts

    3. Introduction Shopping websites forgotten items new but relevant products Buy novel items less frequently

    4. Background Item-based Collaborative Filtering Random Walk Model for Product Recommendation

    5. Item-based Collaborative Filtering vs User-based? Cosine-based similarity(symmetric) Conditional probability based similarity(asymmetric)

    6. Item-based Collaborative Filtering Cosine-based similarity(symmetric): R: user-item matrix R*,i : vector notation of its ith column. Example:

    7. Item-based Collaborative Filtering Conditional probability based similarity(asymmetric): a ? [0, 1] Freq(i): number of users that have purchased item i in the training data R(i, j): (i, j) element in the normalized n × m user-item matrix Example: sim(i, j) = 10 / 4 × 3 (a = 1)

    8. Random Walk Model for Product Recommendation Random Walk Model: P: transition matrix P(i, j): transition probability from the page j to the page i Rn: ranking vector of all the pages d ? (0, 1) is a damping factor U: unit vector Movie recommendation

    9. Basket-Sensitive Random Walk on Bipartite Network First-Order Transition Probability:

    10. Basket-Sensitive Random Walk on Bipartite Network First-Order Transition Probability: Example: I = 2, j = 1, k=2

    11. Basket-Sensitive Random Walk on Bipartite Network First-Order Transition Probability: a1, a2 ? [0, 1]

    12. Basket-Sensitive Random Walk on Bipartite Network =dP(I - dP)-1 Rbasket : basket-based scores Ui basket = 1/m, if the ith product is in the basket 0, otherwise m: number of products in the current basket

    13. Basket-Sensitive Random Walk on Bipartite Network

    14. Experiments Binary Hit Rates with Popularity Based Leave-three-Out Protocol Weighted Hit Rates with Leave-One-Out Protocol

    15. Conclusions and Future Work Propose a basket-sensitive random walk model for personalized recommendation in the grocery shopping domain. Deploy the network-based approach in a live recommender system.

    16. My thoughts How to combine it with association rules?

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