improving recommendation lists through topic diversification
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Improving Recommendation Lists Through Topic Diversification. CaiNicolas Ziegler, Sean M. McNee, Joseph A. Konstan, Georg Lausen WWW \'05 報告人 : 謝順宏. Outline. Introduction On collaborative filtering Evaluation metrics Topic diversification Empirical analysis Related work Conclusion.

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improving recommendation lists through topic diversification

Improving Recommendation Lists ThroughTopic Diversification

CaiNicolas Ziegler, Sean M. McNee,Joseph A. Konstan, Georg Lausen

WWW \'05


  • Introduction
  • On collaborative filtering
  • Evaluation metrics
  • Topic diversification
  • Empirical analysis
  • Related work
  • Conclusion
  • 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.
  • Topic diversification
  • Intra-list similarity metric.
  • Accuracy versus satisfaction.
    • “accuracy does not tell the whole story”
on collaborative filtering cf
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.
user based collaborative filtering
User-based Collaborative Filtering
  • a set of users
  • a set of products
  • partial rating function

for each user,

user based collaborative filtering1
User-based Collaborative Filtering

Two major steps:

  • Neighborhood formation.
    • Pearson correlation
    • Cosine distance
  • Rating prediction
itembased collaborative filtering
Itembased Collaborative Filtering
  • Unlike user-based CF, similarity values c are computed for items rather than users.
evaluation metrics
Evaluation metrics
  • Accuracy Metrics
    • Predictive Accuracy Metrics
    • Decision Support Metrics
  • Beyond Accuracy
    • Coverage
    • Novelty and Serendipity
  • Intra-List Similarity
accuracy metrics
Accuracy Metrics
  • Predictive Accuracy Metrics
    • Mean absolute error (MAE)
    • Mean squared error(MSE)
  • Decision Support Metrics
    • Recall
    • Precision
beyond accuracy
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”.
intra list similarity ils
Intra List Similarity(ILS)
  • To measure the similarity between product
topic diversification
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.
topic diversification1
Topic Diversification
  • Taxonomy-based similarity metric
    • To compute the similarity between product sets based upon their classification.
topic diversification2
Topic Diversification
  • Topic Diversification Algorithm
    • Re-ranking the recommendation list from applying topic diversification.
topic diversification3
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.
recommendation dependency
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.
empirical analysis
Empirical analysis
  • Dataset
    • BookCrossing (
    • 278,858 members
    • 1,157,112 ratings
    • 271,379 distinct ISBN
data clean condensation
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
evaluation framework setup
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.
  • 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.
  • Diversification factor impact
  • Human perception
  • Interaction with accuracy
related work
Related work
  • Northern Light (
  • Google (
  • An algorithmic framework to increase the diversity of a top-N list of recommended products.
  • New intra-list similarity metric.