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WWW'15: A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

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Presented in the WebScience Track of WWW 15 by Elisabeth Lex

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WWW'15: A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

  1. 1. http://Learning-Layers-euhttp://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 1 Austrian Science Fund: P 25593-G22
  2. 2. http://Learning-Layers-eu 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 2
  3. 3. http://Learning-Layers-eu 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 3
  4. 4. http://Learning-Layers-eu SUSTAIN (Love et al., 2004) 4 • 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)
  5. 5. http://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 5
  6. 6. http://Learning-Layers-eu 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 6
  7. 7. http://Learning-Layers-eu 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) 7
  8. 8. http://Learning-Layers-eu 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 8
  9. 9. http://Learning-Layers-eu Results 9 • SUSTAIN+CFU improves CFU on all three datasets • CiteULike: High average topic similarity per user, CFR wins • Delicious: Mutual-fan crawling strategy, WRMF wins
  10. 10. http://Learning-Layers-eu 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 study 10
  11. 11. http://Learning-Layers-eu 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 11
  12. 12. http://Learning-Layers-eu 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 12
  13. 13. http://Learning-Layers-eu Code and Framework 13 • 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
  14. 14. http://Learning-Layers-eu Thank you for your attention! Questions? Elisabeth Lex elisabeth.lex@tugraz.at Ass. Prof. at Graz University of Technology (Austria) Head of Social Computing at Know-Center (Austria) 14

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