attention please! a hybrid resource recommender mimicking attention-interpretation dynamics
TRANSCRIPT
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Learning Layers Scaling up Technologies for Informal Learning in SME Clusters
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics Paul Seitlinger, Dominik Kowald, Simone Kopeinik, Ilire Hasani-Mavriqi, Tobias Ley, Elisabeth Lex
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Austrian Science Fund: P 25593-G22
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
What will this talk be about?
• Resource RecommendaBon (user-‐based CollaboraBve Filtering)
• A computaBonal model of human category learning (SUSTAIN)
• A novel hybrid recommender approach that combines both to further personalize and improve CF
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Why?
• Recommender research exploits digital traces of social acBons and interacBons – E.g. CF suggests resources of most similar users
• EnBBes of different quality (e.g., users, resources, tags) are related to each other
• In CF, users just another enBty • Structuralist simplificaBon • Neglects nonlinear, user-‐resource dynamics that shape a"enBon and interpretaBon
• No ranking of resources in CF 3
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
SUSTAIN (Love et al., 2004)
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• Resource represented by features • Cluster(s) H
– Vector of values along the n feature dimensions
– Fields of interest • A"enBonal weights wi:
– Importance of feature for user • Training (for each resource R)
– Start with one cluster – Form new cluster if sim(R,H) < T – AdjusBng Hj and wi aYer each run
• TesBng – Compare features of candidate to best cluster
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Our Approach: SUSTAIN+CFU
• Step 1: Create candidate set Cu for target user u (top 100 resources of CFU
• Step 2: Train SUSTAIN network of target user u • Step 3: Apply each candidate c of Cu to network • Step 4: Hybrid approach
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Evaluation: Datasets
• Social tagging systems – Freely available for scienBfic purposes – Topics can be easily derived from tagging data
(e.g., Krestel et al., 2010) à Latent Dirichlet AllocaBon (LDA) with 500 topics
• No p-‐core pruning but deleted unique resources
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Evaluation: Method and Metrics
• Training and test-‐set splits • Per user: 20% most recent for tesBng, 80% for training • Retains chronological order
à predict future based on the past • Comparison of top-‐20 recommended resources with relevant resources from test-‐set
• Metrics – nDCG@20 – MAP@20 – Precision / Recall plots (k = 1 – 20)
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Baseline Algorithms
• Most Popular (MP) • User-‐Based CollaboraBve Filtering (CFU) • Resource-‐Based CollaboraBve Filtering (CFR) • Content-‐based Filtering using Topics (CBT) • SUSTAIN+CFU à Available in the open source TagRec framework
• Weighted Regularized Matrix FactorizaBon (WRMF) à MyMediaLite
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Results
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• SUSTAIN+CFU improves CFU on all three datasets • CiteULike: High average topic similarity per user, CFR wins • Delicious: Mutual-‐fan crawling strategy, WRMF wins
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Evaluation: Open Issues • Datasets
– Other Delicious dataset, LastFM, MovieLens – External/other feature (not dependent on LDA)
• Other metrics – Diversity, Serendipity, Coverage
• ComputaBonal Costs – Our experiments showed that our approach is much faster than CFR and especially WRMF
• Although LDA is needed – RunBme experiment is needed + computaBonal complexity
• Online evaluaBon – Learning Layers field study
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Future Work
• Technical – CF-‐independent variant
• RecommendaBons solely based on user-‐specific SUSTAIN network
– Detailed analysis of computaBonal costs • Conceptual
– Dynamic recommendaBon logic • Exploring relaBonship between a"enBonal focus and novelty seeking and use this for recommendaBon
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Take Away Messages
• Our approach SUSTAIN + CFU can improve CF predicBons – More robust in terms of accuracy esBmates – From our observaBon: less complex in terms of computaBonal efforts
• User-‐resource dynamics, if modelled with a connecBonist approach, can help gain a deeper understanding of Web interacBons in terms of a"enBon, categorizaBon and decision making
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h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Code and Framework
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• TagRec framework • hAps://github.com/learning-‐layers/TagRec/
• Framework for developing and evaluaBng new recommender algorithms in folksonomies • Contains our approach, the baseline algorithms and the evaluaBon protocol and metrics • Capable of tag, resource and user recommendaBons
• Used as recommender engine in the Learning Layers EU project • Links to the datasets we used:
– BibSonomy (2013-‐07-‐01): h"p://www.kde.cs.uni-‐ kassel.de/bibsonomy/dumps/ – CiteULike (2013-‐03-‐10): h"p://www.citeulike.org/faq/data.adp – Delicious (2011-‐05-‐01): h"p://files.grouplens.org/datasets/hetrec2011/ hetrec2011-‐
delicious-‐2k.zip
h"p://Learning-‐Layers-‐eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-‐layers.eu
Thank you for your attention!
QuesJons?
Elisabeth Lex
[email protected] Ass. Prof. at Graz University of Technology (Austria) Head of Social CompuBng at Know-‐Center (Austria)
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