www'15: a hybrid resource recommender mimicking attention-interpretation dynamics
TRANSCRIPT
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Learning LayersScaling up Technologies for Informal Learning in SME Clusters
Attention Please!A Hybrid Resource Recommender Mimicking Attention-Interpretation DynamicsPaul Seitlinger, Dominik Kowald, Simone Kopeinik, Ilire Hasani-Mavriqi, Tobias Ley, Elisabeth Lex
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Austrian Science Fund: P 25593-G22
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
What will this talk be about?
• Resource Recommendation (user-based Collaborative Filtering)
• A computational model of human category learning (SUSTAIN)
• A novel hybrid recommender approach that combines both to further personalize and improve CF
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Why?
• Recommender research exploits digital traces of social actions and interactions– E.g. CF suggests resources of most similar users
• Entities of different quality (e.g., users, resources, tags) are related to each other
• In CF, users just another entity• Structuralist simplification• Neglects nonlinear, user-resource dynamics that shape
attention and interpretation• No ranking of resources in CF
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
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
• Attentional weights wi: – Importance of feature for user
• Training (for each resource R)– Start with one cluster– Form new cluster if sim(R,H) < T– Adjusting Hi and wi after each run
• Testing (for each candicate C)– Compare features of candidate to best
cluster (Hmax)
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
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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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Evaluation: Datasets• Social tagging systems– Freely available for scientific purposes– Topics can be easily derived from tagging data (e.g., Krestel et al., 2010)Latent Dirichlet Allocation (LDA) with 500 topics
• No p-core pruning but deleted unique resources
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Evaluation: Method and Metrics
• Training and test-set splits• Per user: 20% most recent for testing, 80% for training• Retains chronological order
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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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Baseline Algorithms• Most Popular (MP)• User-Based Collaborative Filtering (CFU)• Resource-Based Collaborative Filtering (CFR)• Content-based Filtering using Topics (CBT)• SUSTAIN+CFU
Available in the open source TagRec framework
• Weighted Regularized Matrix Factorization (WRMF) MyMediaLite
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
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
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Evaluation: Open Issues• Datasets
– Other Delicious dataset, LastFM, MovieLens– External/other feature (not dependent on LDA)
• Other metrics– Diversity, Serendipity, Coverage
• Computational Costs– Our experiments showed that our approach is much faster than
CFR and especially WRMF• Although LDA is needed
– Runtime experiment is needed + computational complexity• Online evaluation
– Learning Layers field study10
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Future Work
• Technical– CF-independent variant• Recommendations solely based on user-specific
SUSTAIN network– Detailed analysis of computational costs
• Conceptual– Dynamic recommendation logic• Exploring relationship between attentional focus and
novelty seeking and use this for recommendation
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Take Away Messages
• Our approach SUSTAIN + CFU can improve CF predictions– More robust in terms of accuracy estimates– From our observation: less complex in terms of
computational efforts• User-resource dynamics, if modelled with a
connectionist approach, can help gain a deeper understanding of Web interactions in terms of attention, categorization and decision making
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http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Code and Framework
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• TagRec framework• https://github.com/learning-layers/TagRec/
• Framework for developing and evaluating new recommender algorithms in folksonomies• Contains our approach, the baseline algorithms and the evaluation protocol and metrics• Capable of tag, resource and user recommendations
• Used as recommender engine in the Learning Layers EU project• Links to the datasets we used:
– BibSonomy (2013-07-01): http://www.kde.cs.uni- kassel.de/bibsonomy/dumps/ – CiteULike (2013-03-10): http://www.citeulike.org/faq/data.adp – Delicious (2011-05-01): http://files.grouplens.org/datasets/hetrec2011/ hetrec2011-
delicious-2k.zip
http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – [email protected]
Thank you for your attention!
Questions?
Elisabeth [email protected]
Ass. Prof. at Graz University of Technology (Austria)Head of Social Computing at Know-Center (Austria)
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