www'15: a hybrid resource recommender mimicking attention-interpretation dynamics

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.euhttp://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu

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

1

Austrian Science Fund: P 25593-G22

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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)

http://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

5

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://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 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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://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

• 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 – layers@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

http://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 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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@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

http://Learning-Layers-eu – Scaling up Technologies for Informal Learning in SME Clusters – layers@learning-layers.eu

Thank you for your attention!

Questions?

Elisabeth Lexelisabeth.lex@tugraz.at

Ass. Prof. at Graz University of Technology (Austria)Head of Social Computing at Know-Center (Austria)

14