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

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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.

    Empirical analysis1

    Empirical analysis

    • ΘF=0,

    Empirical analysis2

    Empirical analysis

    Empirical analysis3

    Empirical analysis



    • 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.

    Empirical analysis4

    Empirical analysis



    • Diversification factor impact

    • Human perception

    • Interaction with accuracy

    Multiple linear regression

    Multiple Linear Regression

    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.

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