Improving Recommendation Lists Through Topic Diversification

1. Improving Recommendation Lists ThroughTopic Diversification CaiNicolas Ziegler, Sean M. McNee,Joseph A. Konstan, Georg Lausen WWW '05 報告人:謝順宏

2. Outline • Introduction • On collaborative filtering • Evaluation metrics • Topic diversification • Empirical analysis • Related work • Conclusion

3. Introduction • To reflect the user’s complete spectrum of interests. • Improves user satisfaction. • Many recommendations seem to be “similar” with respect to content. • Traditionally, recommender system projects have focused on optimizing accuracy using metrics such as precision/recall or mean absolute error.

4. Introduction • Topic diversification • Intra-list similarity metric. • Accuracy versus satisfaction. • “accuracy does not tell the whole story”

5. On collaborative filtering(CF) • Collaborative filtering (CF) still represents the most commonly adopted technique in crafting academic and commercial recommender systems. • Its basic idea refers to making recommendations based upon ratings that users have assigned to products.

6. User-based Collaborative Filtering • a set of users • a set of products • partial rating function for each user,

7. User-based Collaborative Filtering Two major steps: • Neighborhood formation. • Pearson correlation • Cosine distance • Rating prediction

8. Itembased Collaborative Filtering • Unlike user-based CF, similarity values c are computed for items rather than users.

9. Evaluation metrics • Accuracy Metrics • Predictive Accuracy Metrics • Decision Support Metrics • Beyond Accuracy • Coverage • Novelty and Serendipity • Intra-List Similarity

10. Accuracy Metrics • Predictive Accuracy Metrics • Mean absolute error (MAE) • Mean squared error(MSE) • Decision Support Metrics • Recall • Precision

11. Beyond Accuracy • Coverage • Coverage measures the percentage of elements part of the problem domain for which predictions can be made. • Novelty and Serendipity • Novelty and serendipity metrics thus measure the “non-obviousness” of recommendations made, avoiding “cherry-picking”.

12. Intra List Similarity(ILS) • To measure the similarity between product

13. Topic Diversification “Law of Diminishing Marginal Returns” • Suppose you are offered your favorite drink. Let p1 denote the price you are willing to pay for that product. Assuming your are offered a second glass of that particular drink, the amount p2 of money you are inclined to spend will be lower, i.e., p1 > p2. Same for p3, p4, and so forth.

14. Topic Diversification • Taxonomy-based similarity metric • To compute the similarity between product sets based upon their classification.

15. Topic Diversification • Topic Diversification Algorithm • Re-ranking the recommendation list from applying topic diversification.

16. Topic Diversification • ΘF defines the impact that dissimilarity rank exerts on the eventual overall output. • Large ΘF favors diversification over a’s original relevance order. • The input lists muse be considerably larger than the final top-N list.

17. Recommendation dependency • We assume that recommended products along with their content descriptions, only relevance weight ordering must hold for recommendation list items, no other dependencies are assumed. • An item b’s current dissimilarity rank with respect to preceding recommendations plays an important role and may influence the new ranking.

18. Empirical analysis • Dataset • BookCrossing (http://www.bookcrossing.com) • 278,858 members • 1,157,112 ratings • 271,379 distinct ISBN

19. Data clean & condensation • Discarded all books missing taxonomic descriptions. • Only community members with at least 5 ratings each were kept. • 10339 users • 6708books • 316349 ratings

20. Evaluation Framework Setup • Did not compute MAE metric values • Adopted K-folding (K=4) • We were interested in seeing how accuracy, captured by precision and recall, behaves whe increasing θF.

21. Empirical analysis • ΘF=0,

22. Empirical analysis

23. Empirical analysis

24. Conclusion • We found that diversification appears detrimental to both user-based and item-based CF along precision and recall metrics. • Item-based CF seems more susceptible to topic diversification than user-based CF, backed by result from precision, recall and ILS metric analysis.

25. Empirical analysis

26. Conclusion • Diversification factor impact • Human perception • Interaction with accuracy

27. Multiple Linear Regression

28. Related work • Northern Light (http://www.northernlight.com) • Google (http://www.google.com)

29. Conclusion • An algorithmic framework to increase the diversity of a top-N list of recommended products. • New intra-list similarity metric.