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iTALS: implicit tensor factorization for context-aware recommendations (ECML/PKDD 2012 presentation)

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This presentation is about the context-aware recommender algorithm iTALS.
iTALS is a context-aware recommender algorithm for implicit feedback data. The user-item-context(s) setup is modelled in a binary tensor. Weights are also assigned to the cells based on the certainity of their information. An ALS-based algorithm is proposed that is capable of efficiently factorizing this tensor. Additionally a novel context information is introduced: sequentiality. This context allows us to incorporate association rule like information into the factorization framework and to differentiate between items with different repetetiveness patters and thus to make recommendations more accurate.
This presentation was originally given at ECML/PKDD 2012 in Bristol.

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iTALS: implicit tensor factorization for context-aware recommendations (ECML/PKDD 2012 presentation)

  1. 1. iTALSFast ALS-based tensor factorization for context-awarerecommendation from implicit feedbackBalázs Hidasi – balazs.hidasi@gravityrd.comDomonkos Tikk – domonkos.tikk@gravityrd.com ECML/PKDD, 25TH SEPTEMBER 2012, BRISTOL
  2. 2. Overview• Implicit feedback problem• Context-awareness ▫ Seasonality ▫ Sequentaility• iTALS ▫ Model ▫ Learning ▫ Prediction• Experiments
  3. 3. Feedback types• Feedback: user-item interraction (events)• Explicit: ▫ Preferences explicitely coded ▫ E.g.: Ratings• Implicit: ▫ Preferences not coded explicitely ▫ E.g.: purchase history
  4. 4. Problems with implicit feedback• Noisy positive preferences ▫ E.g.: bought & disappointed• No negative feedback available ▫ E.g.: had no info on item• Usually evaluated by ranking metrics ▫ Can not be directly optimized
  5. 5. Why to use implicit feedback?• Every user provides• Some magnitudes larger amount of information than explicit feedback• More important in practice ▫ Explicit algorithms are for the biggest only
  6. 6. Context-awareness• Context: any information associated with events• Context state: a possible value of the context dimension• Context-awareness ▫ Usage of context information ▫ Incorporating additional informations into the method ▫ Different predictions for same user given different context states ▫ Can greatly outperform context-unaware methods  Context segmentates items/users well
  7. 7. Seasonality as context• Season: a time period ▫ E.g.: a week• Timeband: given interval in season ▫ Context-states ▫ E.g.: days• Assumed: ▫ aggregated behaviour in a given timeband is similar inbetween seasons User Item Date Context ▫ and different for different 1 A 12/07/2010 1 timebands 2 B 15/07/2010 3 ▫ E.g.: daily/weekly routines 1 B 15/07/2010 3 … … … 1 A 19/07/2010 1
  8. 8. Sequentiality• Bought A after B ▫ B is the context-state of the user’s event on A• Usefullness ▫ Some items are bought together ▫ Some items bought repetetively ▫ Some are not• Association rule like information incorporated into model as context ▫ Here: into factorization methods• Can learn negated rules ▫ If C then not A
  9. 9. M3iTALS - Model• Binary tensor ▫ D dimensional User ▫ User – item – context(s) M1 T• Importance weights ▫ Lower weights to zeroes (NIF) ▫ Higher weights to cells with Item more events M2• Cells approximated by sum of the values in the elementwise product of D vectors ▫ Each for a dimension ▫ Low dimensional vectors
  10. 10. iTALS - Learning•
  11. 11. iTALS - Prediction• Sum of values in elementwise product of vectors ▫ User-item: scalar product of feature vectors ▫ User-item-context: weighted scalar product of feature vectors• Context-state dependent reweighting of features• E.g.: ▫ Third feature = horror movie ▫ Context state1 = Friday night  third feature high ▫ Context state2 = Sunday afternoon  third feature low
  12. 12. Experiments• 5 databases ▫ 3 implicit  Online grocery shopping  VoD consumption  Music listening habits ▫ 2 implicitizied explicit  Netflix  MovieLens 10M• Recall@20 as primary evaluation metric• Baseline: context-unaware method in every context- state
  13. 13. Scalability Running times on the Grocery dataset 350 300 iALS epochRunning time (sec) 250 iTALS (time bands) epoch 200 iTALS (sequence) epoch 150 iALS calc. matrix 100 iTALS (time bands) calc. matrix 50 iTALS (sequence) calc. matrix 0 10 20 30 40 50 60 70 80 90 100 Number of factors (K)
  14. 14. Results – Recall@200.12000.10000.08000.06000.04000.02000.0000 VoD Grocery LastFM Netflix MovieLens iALS (MF method) iCA baseline iTALS (seasonality) iTALS (sequence)
  15. 15. Results – Precision-recall curves0.25 Recall-precision (Grocery)0.200.150.10 iALS0.050.00 iCA baseline 0.00 0.05 0.10 0.15 0.20 Recall-precision (VoD) iTALS0.08 (season)0.06 iTALS (sequence)0.040.020.00 0.00 0.05 0.10 0.15 0.20
  16. 16. Summary• iTALS is a ▫ scalable ▫ context-aware ▫ factorization method ▫ on implicit feedback data• The problem is modelled ▫ by approximating the values of a binary tensor ▫ with elementwise product of short vectors ▫ using importance weighting• Learning can be efficiently done by using ▫ ALS ▫ and other computation time reducing tricks• Recommendation accuracy is significantly better than iCA-baseline• Introduced a new context type: sequentiality ▫ association rule like information in factorization framework
  17. 17. Thank you for your attention!For more of my recommender systems related research visit my website: http://www.hidasi.eu T

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