The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.

1. Intent-Aware Diversification
Using a Constrained PLSA
Jacek Wasilewski and Neil Hurley
Insight Centre for Data Analytics, University College Dublin, Ireland

2. Diversity Frameworks
• Different diversity frameworks have been proposed:
– Intra-List Diversity [Ziegler2005, Zhang2008]
– Proportionality Framework [Dang2012]
– Intent-Aware Framework [Agrawal2009, Santos2010,
Vargas2011]
• Each framework promotes different type of diversity.
• The Intent-Aware framework depends on explicit aspects that each
item is described by.
Users’ needs are expressed in terms of those aspects as well.
• Explicit aspects make the process and results easier to interpret.
• In movies domain, genres can be treated as aspects.
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3. Intent-Aware Diversity
• Intuition:
– users have different aspect preferences,
– item selection depends on current intent,
– aspects should not be over-represented.
• Can be achieved using xQuAD re-ranker [Santos2010, Vargas2011]:
•
𝑖∗ = arg max
)∈+,.
1 − 𝜆 𝑠 𝑢, 𝑖 + 𝜆 6 𝑝 𝑎 𝑢 𝑝 𝑖 𝑎, 𝑢 9 1 − 𝑝 𝑗 𝑎, 𝑢
<∈=>∈𝒜
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 3
accuracy term diversity term
1
1
2
2
3
3

4. • The aspect model [Hofmann2004]:
𝑝 𝑖 𝑢 = 6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
Probability Components
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 4
6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
Aspect model creation can be understood as
fitting a model predicting 𝑝(𝑖|𝑢) in terms of
aspects.

5. • The aspect model [Hofmann2004]:
𝑝 𝑖 𝑢 = 6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
Probability Components
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 5
6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
[Vargas2011] (ExAs-CoO)
• Explicit aspects
• Ad-hoc probabilities
[Hofmann2004] (PLSA)
• Latent aspects
• Inferred probabilities

6. • The aspect model [Hofmann2004]:
𝑝 𝑖 𝑢 = 6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
Probability Components
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 6
[Vargas2011] (ExAs-CoO)
• Explicit aspects
• Ad-hoc probabilities
[Hofmann2004] (PLSA)
• Latent aspects
• Inferred probabilities
6 𝑝 𝑎 𝑢 𝑝(𝑖|𝑎, 𝑢)
>∈𝒜
Constrained pLSA (c-PLSA)
• Explicit aspects
• Inferred probabilities

7. Model Predictive Performance
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 7
ExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoOExAs−CoO
c−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSAc−PLSA
PLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSAPLSA
0.09
0.10
0.11
0.12
0.13
0.14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Number of latent features
Precision@10

8. Re-ranking Performance
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0.12
0.14
0.16
0.18
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Lambda
Precision@10
Method
xQuAD (PLSA)
xQuAD (c−PLSA)
xQuAD (ExAs−CoO)
Matrix Factorisation, MovieLens 1M
@10
Accuracy Diversity
Precision nDCG 𝛂-nDCG S-recall
MF 0.168 0.274 0.278 0.157
+ExAs-CoO 0.161 0.258 0.294 0.171
+c-PLSA 0.173 0.281 0.289 0.162
+PLSA 0.174 0.283 0.288 0.162
Order from the predictive
performance analysis is kept.
c-PLSA is close to PLSA.
ExAs-CoO seems to offer the
best diversity performance
– due to model biases.
Framework: http://ranksys.org

9. Final Remarks
• c-PLSA – probabilistic aspect model using explicit aspects, optimised for
predictive performance.
• More accurate estimates = more accurate estimate of the IA objective.
• Diversity comes from the aspect model, not the IA objective.
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 9
0.36
0.37
0.38
0.39
0.00 0.25 0.50 0.75 1.00
Lambda
Alpha−nDCG@20
Method
xQuAD
No redundancy
User−Based kNN, MovieLens 20M
No redundancy: 𝑖∗ = arg max
)∈+,.
1 − 𝜆 𝑠 𝑢, 𝑖 + 𝜆 6 𝑝 𝑎 𝑢 𝑝 𝑖 𝑎, 𝑢 9 1 − 𝑝 𝑗 𝑎, 𝑢
<∈=>∈𝒜

10. Thank you for your attention!
RecSys’16 Intent-Aware Diversification Using a Constrained PLSA 10

11. References
• Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying Search
Results. WSDM 2009.
• Dang, V., & Croft, W. B. (2012). Diversity by Proportionality: An Election-based
Approach to Search Result Diversification. ACM SIGIR 2012.
• Hofmann, T. (2004). Latent Semantic Models for Collaborative Filtering. ACM
TIS 22(1).
• Santos, R. L. T., Macdonald, C., & Ounis, I. (2010). Exploiting Query
Reformulations for Web Search Result Diversification. ACM WWW 2010.
• Vargas, S., Castells, P., & Vallet, D. (2011). Intent-oriented Diversity in
Recommender Systems. ACM SIGIR 2011.
• Zhang, M., & Hurley, N. (2008). Avoiding Monotony: Improving the Diversity of
Recommendation Lists. ACM RecSys 2008.
• Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving
Recommendation Lists Through Topic Diversification. ACM WWW 2005.
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