Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation Takuya Kitazawa Graduate School of Information Science and Technology, The University of Tokyo RecProfile 2016 

Incremental Factorization Machines for (1) Persistently Cold-starting, (2) Online Item Recommendation as new baseline method Factorization Machines context-aware recommender Incremental MF online item recommender optimized by SGD 

Continuously recommend ”new” items to ”unforeseen” users Poor recommendation accuracy ! Yahoo! Labs — online ad [M. Aharon et al. RecSys 2013] Booking.com — hotel reservation [L. Bernardi et al. CBRecSys 2015]
 Rakuten — golf package reservation [R. Swezey and Y. Chung. CIKM 2015] 3 Challenge I: Persistent cold-start in e-commerce users’ rare activity changing persona short-lived items changing prices 

4 Promising solution: Context-aware (feature-based) recommendation Occupation Browser Age Sex [ 0, 0, 1, 0, 0, 0, 0, 1, 22, 1 ] [ 0, 1, 1, 0, 500 ] Category Price Enriched inputs 

For 5 Factorization Machines (FMs; general predictor) for 
 context-aware recommendation global bias linear interaction [S. Rendle. ACM TIST, 3(3), 2012.] (Fig. 1) MF FMs 

For 6 Factorization Machines (FMs; general predictor) for 
 context-aware recommendation global bias linear interaction [S. Rendle. ACM TIST, 3(3), 2012.] (Fig. 1) MF FMs Persistent cold-start can be seen as:
 concept drift online algorithms are more effective 

7 Challenge II: Online item recommendation Incrementally update model based on user-item interactions (e.g. click, buy, book; not explicit rating) 

8 Classical static baseline: Matrix Factorization (MF) for item recommendation https://en.wikipedia.org/wiki/Collaborative_filtering R ≈ P users QT items × 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1.0 least-squared loss for binary output 

9 Incremental Matrix Factorization (iMF) for 
 online item recommendation iMF S can be data stream update (user ID) update (item ID) for each observation Where are auxiliary features? [J. Vinagre et al. UMAP 2014] SGD update 

10 Overview FMs (context-aware recommender) iMF (online item recommender) Incremental FMs (iFMs) (context-aware online item recommender) optimized by SGD as generic framework (i.e. baseline) classical online baseline modern context-aware baseline 

11 Using FMs to item recommendation Solve MF with unique target value
 [J. Vinagre et al. In Proc. of UMAP 2014, pp. 459–470.] Solve FM as regression (least-square loss) with y = 1.0 1.0 For 1.0 

12 Generalizing iMF to incremental FMs (iFMs) SGD update for FMs O( #non-zero in x × k ) S is data stream for each observation SGD update for MF update (user information) update (item information) update update update SGD update SGD update 

13 Incremental adaptive regularization FMs have many hyperparameters: Adaptive regularization [S. Rendle. In Proc. of WSDM 2012, pp. 133-142.] Incremental adaptive regularization (“pseudo validation sample” idea) outdated immediately 

14 Evaluation of online item recommender: Test-then-learn procedure 0) pre-train with initial 20% of samples [J. Vinagre, et al. In Proc. of REDD 2014.] 
 Evaluation of Recommender Systems in Streaming Environments, (u1 , i1 ) ɾɾɾ timestamped n-samples for evaluation ɾɾɾ (uk , ik ) (uk+3 , ik+3 ) For sample (u, i) 1) top-N recommendation for u 2) check if i is in top-N list 3) compute recall@N in 4) update model based on (u, i) (un , in ) 

15 Experiment: MovieLens 100k (1/2) Real-world movie rating dataset ■ Binarized: { 1, 2, 3, 4, 5 } => { 0, 1 } ■ Users’ rare activity 

16 Experiment: MovieLens 100k (2/2) MF < FMs & static < incremental 

17 Experiment: Synthetic click (1/2) 5 ad variants w/ categories: { ad1, ad2 }, { ad3, ad4 }, { ad5 }
 ■ Rule-based generator from [M. Aharon et al., RecSys2013]
 
 
 ■ Item instability 500k impressions ▶ rule change ▶ 500k impressions 

18 Experiment: Synthetic click (2/2) iFMs show higher adaptivity for changes 

19 Overall results (5 times w/ different initial params.) Trade-off : efficiency vs context-awareness FMs are: slower better 

20 Summary ■ Key features: 
 positive-only feedback
 incremental adaptive regularization ■ Use your appropriate competitor: FMs (context-aware recommender) iMF (online item recommender) Incremental FMs (iFMs) (context-aware online item recommender) ID-only Feature-based Static MF FMs Online iMF iFMs 

21 Future work ▶ More competitors/datasets (time-stamped, rich in contexts) ▶ Other approaches for positive-only feedback (e.g. BPR) [J. Vinagre, et al. In Proc. of UMAP 2014.] (Fig. 1) 

Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation Takuya Kitazawa Graduate School of Information Science and Technology, The University of Tokyo RecProfile 2016 

25 500k impressions ▶ rule change ▶ 500k impressions 3 [M. Aharon et al., 
 RecSys2013] (Table 1) ⾢ most clicked ad rule 

26 New feature insertion e.g. insert new user’s dimension