Incorporating Diversity in a Learning to Rank Recommender System

Jacek Wasilewski and Neil Hurley

Insight Centre for Data Analytics, University College Dublin, Ireland

Recommender problem

Incorporating Diversity in a Learning to Rank Recommender System 2

If I watched

what should I watch next (that I will like)?

FLAIRS-29

Recommender problem

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I watched: My recommendations:

Recommender problem

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I watched: My recommendations:

Recommended movies are:• well-known,• very similar to what I have

seen in the past,• very similar to each

other.

Beyond accuracy: the diversity problem

• High similarity of the recommended items might not satisfy the users and generally lead to a poor user experience.

• Few reasons:

– recommendations that are too obvious and of little help,

– complexity of user's needs not recorded by the system,

– uncertainty of current user's needs.

• Introducing diversity addresses some of these aspects.

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Beyond accuracy: the diversity problem

• Diversity – dissimilarity of items being recommended.

• Intra-list diversity (ILD) – average pairwise distance – measures the diversity of a recommendation:

ILD ℛ =1

ℛ ℛ − 1 ) 𝑑(𝑖, 𝑗)0,1∈ℛ

• For the previously used example of Star Wars and Indiana Jones movies:ILD ℛ = 0.06

• How recommendations can be diversified?

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Beyond accuracy: the diversity problem

• A common approach is results diversification / re-ranking:

• The re-ranking objective is to find an optimal set ℛ that:ℛ∗ = argmax

ℛ⊆𝒞1 − 𝜆 𝑎𝑐𝑐 ℛ + 𝜆𝑑𝑖𝑣(ℛ)

• We are interested if diverse recommendations can be achieved without re-ranking while learning.

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𝒞 ℛ

Research goals

• Continue and explore the work of (Hurley, 2013) on incorporating diversity into a collaborative filtering algorithm.

• The main interest is focused on:

– model-based matrix factorisation algorithm,

– alternating least squares (ALS) factorisation technique,

– the personalised ranking objective - RankALS (Takács, 2012),

– incorporating diversity using regularisation.

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Matrix factorisation & Learning to Rank

• Learning for rating prediction:

ℒ 𝑃, 𝑄 =) 𝑟G0 − 𝑝GI𝑞0 K

G,0• Learning for ranking:

ℒ 𝑃, 𝑄 = ) (𝑟G0 − 𝑟G1) − (𝑝GI𝑞0 − 𝑝GI𝑞1)K

G,0,1• We will refer to this as the accuracy objective.

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≈ ×

R P Q

𝑝G 𝑞0𝑟G0user vector item vector

Learning to Rank with diversity

• In the previous work, (Hurley, 2013) tried to modify the accuracy objective to increase diversity of recommendation sets.

• Our approach aims to incorporate diversity using regularisation:ℒ 𝑃,𝑄 + 𝜆reg(𝑃,𝑄)

• Regularisation typically has been used to control for overfitting.

• Different types of regularisers have been proposed to incorporate other side information, like social networks, to support the recommendation and increase its accuracy.

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Learning to Rank with diversity

• Social networks (Jamali, 2010):

• LapDQ diversity regulariser:

FLAIRS-29 Incorporating Diversity in a Learning to Rank Recommender System 11

𝑠𝑖𝑚(𝑢, �⃗�)𝑢

�⃗�

�⃗�G − �⃗�S K

�⃗�G

�⃗�S

𝑠𝑖𝑚(𝚤, 𝚥)𝚤

𝚥

�⃗�0 − �⃗�1K

�⃗�0

�⃗�1

reg 𝑃, 𝑄 = )𝑑 𝑖, 𝑗 𝑞0 − 𝑞1K

0,1

reg 𝑃,𝑄 =)𝑠𝑖𝑚 𝑢,𝑣 𝑝G − 𝑝S K

G,S

reg 𝑃,𝑄 =)𝑠𝑖𝑚 𝑢,𝑣 𝑝G − 𝑝S K

G,S

Learning to Rank with diversity

• Social networks (Jamali, 2010):

• LapDQ diversity regulariser:

FLAIRS-29 Incorporating Diversity in a Learning to Rank Recommender System 12

𝑠𝑖𝑚(𝑢, �⃗�)𝑢

�⃗�

�⃗�G − �⃗�S K

�⃗�G

�⃗�S

𝑠𝑖𝑚(𝚤, 𝚥)𝚤

𝚥

�⃗�0 − �⃗�1K

�⃗�0

�⃗�1

reg 𝑃, 𝑄 = )𝑑 𝑖, 𝑗 𝑞0 − 𝑞1K

0,1

• LapDQ: reg 𝑃, 𝑄 = ∑ 𝑑 𝑖, 𝑗 𝑞0 − 𝑞1K

0,1 == 2 ∑ 𝑑0 .0 𝑞0 K − ∑ 𝑑 𝑖, 𝑗 𝑞0I𝑞10,1

• PLapDQ: reg 𝑃, 𝑄 = ∑ 𝑑 𝑖, 𝑗 𝑝GI 𝑞0 − 𝑞1K

G,0,1

• DQ: reg 𝑃, 𝑄 = ∑ 𝑑 𝑖, 𝑗 𝑞0I𝑞10,1

Experimental setup

• Datasets:

– Netflix, MovieLens 20m

• Recommendation algorithms:

– RankALS (baseline)

– Random (for reference)

• Diversification algorithms:

– Greedy re-ranker (MMR)

– Regularisation:DQ, LapDQ, PLapDQ

• Ranked lists of top 20.

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• Accuracy metrics:Precision, Recall, nDCG

• Rank-aware diversity metrics (Vargas, 2011) :

– EILD – intra-list diversity1

ℛ ℛ − 1 ) 𝑑𝑖𝑠𝑐(𝑘0)𝑑(𝑖, 𝑗)0,1∈ℛ

– EPD – profile distance1

ℛ ℐG)) 𝑑𝑖𝑠𝑐(𝑘0)𝑑 𝑖, 𝑗

1∈ℐZ0∈ℛPowered by: RankSys framework (http://ranksys.org)Source code: https://github.com/jacekwasilewski/RankSys-DivMF

Results

• The LapDQ regulariser dominates other regularisers.

• Adding user's information does not improve LapDQ regulariser.

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random

0.60

0.63

0.66

0.69

0.72

0.75

0.03 0.04 0.05 0.06 0.07 0.08 0.09nDCG@20

EILD

@20

RegulariserLapDQDQPLapDQ

MovieLens 20m

random

0.64

0.66

0.68

0.70

0.72

0.74

0.76

0.78

0.80

0.04 0.05 0.06 0.07 0.08 0.09 0.10nDCG@20

EILD

@20

RegulariserLapDQDQPLapDQ

Netflix

Results

• All regularisers improve diversity over the baseline - best trade-off seems to be offered by LapDQ.

• Accuracy is sacrificed with the increase in diversity.

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Netflix nDCG EILD EPD

Random 0.0015 0.7885 0.7643

RankALS 0.1002 0.6367 0.6721

+LapDQ 0.0811 0.7354 0.7365

+DQ 0.0764 0.6872 0.7390

+PLapDQ 0.0826 0.6943 0.7385

MovieLens nDCG EILD EPD

Random 0.0008 0.7505 0.7430

RankALS 0.0951 0.5935 0.6207

+LapDQ 0.0777 0.6829 0.7013

+DQ 0.0792 0.6479 0.6634

+PLapDQ 0.0816 0.6226 0.6471

best reg.

> baseline

> random

best

• A greedy re-ranker outperforms best regularisers in terms of accuracy-diversity trade-off.

• Possible reason is that global regulariser does not necessarily improve the diversity of each individual user – the average diversity is being optimised, some users experience a decrease in diversity.

Netflix nDCG EILD EPD

Random 0.0015 0.7885 0.7643

RankALS 0.1002 0.6367 0.6721

+MMR 0.0959 0.7662 0.7164

+LapDQ 0.0811 0.7354 0.7365

Results

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MovieLens nDCG EILD EPD

Random 0.0008 0.7505 0.7430

RankALS 0.0951 0.5935 0.6207

+MMR 0.0897 0.7336 0.6717

+LapDQ 0.0777 0.6829 0.7013

best div.

> baseline

> random

best

Summary

• We proposed a way of incorporating diversity into the training phase of a model-based algorithm.

• Diversity enhancement is achieved with the use of regularisation.

• Experimental evaluation has been performed on two datasets, using evaluation framework proposed by (Vargas, 2011).

• Diversity regularisers improve diversity of the recommendations, however do not outperform re-ranking approach.

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Thank you!

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References

• Hurley, N. J. (2013). Personalised Ranking with Diversity. RecSys ’13• Vargas, S., & Castells, P. (2011). Rank and Relevance in Novelty and

Diversity Metrics for Recommender Systems. RecSys ’11• Takács, G., & Tikk, D. (2012). Alternating Least Squares for Personalized

Ranking. RecSys ’12• Jamali, M., & Ester, M. (2010). A matrix factorization technique with

trust propagation for recommendation in social networks. RecSys ’10

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