Diversity and novelty for recommendation system

A simple survey of Diversity and
novelty metrics for recommender
systems

Reporter: 孙建凯
2012.07.11

Move beyond accuracy metrics
 while the majority of algorithms proposed in
recommender systems literature have focused on
improving recommendation accuracy
 other important aspects of recommendation
quality, such as the diversity of recommendations,
have often been overlooked.
 The recommendations that are most accurate
according to the standard metrics are sometimes
not the recommendations that are most useful to
users[1]

2
Copyright © 2012 by IRLAB@SDU

Diversity and Novelty

 Accurate is not always good: How Accuracy
Metrics have hurt Recommender Systems
GroupLensResearch,CHI'06


Accuracy does not tell the whole story


Diversity

 Individual Diversity  Aggregate Diversity


Individual Diversity
 Diversity Difficulty[3]
 Average dissimilarity between all pairs of items
recommended to a given user(intra-list similarity)
[2,4]


Diversity Difficulty
 What We Talk About When We Talk About
Diversity [DDR’12 Northeastern University USA]
 Like query difficulty in IR
 For a specific query and corpus, query difficulty is
a measure of how successful the average search
engine should be at ad-hoc retrieval.


Diversity Difficulty
 Diversity Difficulty is defined with respect to a
query and a corpus.
 Describes diversity-the number of subtopics which
are covered by a list;
 Describes novelty-which is inversely proportional
to the number of times a list repeats a subtopic


Finding needles in the haystack
 Imagine a query with 10 subtopics ,1000 documents
relevant to only the first subtopic, and each of the
remaining subtopics covered by a single, unique
document.
 On the other hand ,if there are large numbers of
documents relevant to multiple subtopics, it would
be easy to produce a diversity list.


Diversity Difficulty function
 The maximum amount of diversity achievable by
any ranked list-dmax
 The ease with a system can produce a diverse
ranked list.-dmean
 Harmonic function


Examples


Improving Recommendation Lists Through
Topic Diversification

 Introduce the intra-list similarity metric to access
the topic diversification of recommendation lists
and the topic diversification approach for
decreasing the intra-list similarity
 Average dissimilarity between all pairs of items
recommended to a given user


Intra-list Similarity


Taxonomy-based similarity Metrics
 Instantiate c with their metric for taxonomy-
driven filtering.[5]


Topic Diversification Algorithm
Algorithm A brief textual sketch


Experiments

 precision  diversity

Aggregate Diversity
 improving recommendation Diversity using ranking-
based techniques[IEEE transaction’12]
 Use the total number of distinct items
recommended across all users as an aggregate
diversity measure, define as follows:


General overview of ranking-based
approaches for improving diversity


Re-Ranking Approach


Other Re-ranking Approach


Combining Ranking Approaches
 Many possible ways to combine several ranking
functions
 In this paper , linear combination
 Open issue: letor ? Neural network?


Entropy
 A study of Heterogeneity in Recommendations for
a social Music Service[6]


Open issue:probability


Entropy
Aggregate Entropy: Individual Entropy:
 Item popularity  subtopic popularity?
between lists?


Bipartite network
 Bipartite network projection and personal
recommendation[Tao Zhou, Physical Review]
 Solving the apparent diversity-accuracy dilemma of
recommender systems[Tao Zhou]


Illustration of resource-allocation
process in bipartite network


Solving the apparent diversity-accuracy
dilemma
heats probs


Hybrid Methods
weight hybrid


Diversity Measure


Surprisal/novelty


Results-why better?


Surprise me
 Tangent: A novel, ‘surprise me’, recommendation
algorithm [kdd’09]


Framework of Tangent Algorithm
 Suggest items which are not only relevant to user
preference but also have a large connectivity to
other groups.
 Consisting three parts as follows:
 1 Calculate relevance score(RS) for each node
 2 Calculate bridging score(BRS) for each node
 3 Compute the Tangent score by somehow merging
two criteria above


Case study


Call for papers
 September 20, 2012


Reference
 1. Accurate is not always good: How Accuracy
Metrics have hurt Recommender Systems
 2.improving recommendation Diversity using
ranking-based techniques
 3. What We Talk About When We Talk About
Diversity
 4. Improving Recommendation Lists Through Topic
Diversification
 5. Taxonomy-driven computation of product
recommendations


Reference
 6. A study of Heterogeneity in Recommendations
for a social Music Service
 7. Bipartite network projection and personal
recommendation
 8.Solving the apparent diversity-accuracy dilemma
of recommender systems
 9. Tangent: A novel, ‘surprise me’, recommendation
algorithm


 thanks


Diversity and novelty for recommendation system

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