Executive summary: The paper deals with the implicit feedback problem for recommender systems, and propose a fast context-aware tensor factorization method that can integrate any kind of contextual information. Paper abstract: Albeit the implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we pro- pose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALSapplies a fast, ALS-basedtensor factorization learn- ing method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contex- tual information into the model while maintaining its computational effi- ciency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behav- ior in different time intervals. The other views the user history as sequen- tial information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and “implicitized” Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.

1. iTALS
Fast ALS-based tensor factorization for context-aware
recommendation from implicit feedback
Balázs Hidasi – balazs.hidasi@gravityrd.com
Domonkos Tikk – domonkos.tikk@gravityrd.com

2. Overview
• Implicit feedback problem
• Context-awareness
▫ Seasonality
▫ Sequentaility
• iTALS
▫ Model
▫ Learning
▫ Prediction
• Experiments

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. 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. 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. 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. 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. 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. M3
iTALS - 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. iTALS - Learning
•

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. 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. Scalability
Running times on the Grocery dataset
350
300
iALS epoch
Running 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. Results – Recall@20
0.1200
0.1000
0.0800
0.0600
0.0400
0.0200
0.0000
VoD Grocery LastFM Netflix MovieLens
iALS (MF method) iCA baseline iTALS (seasonality) iTALS (sequence)

15. Results – Precision-recall curves
0.25 Recall-precision (Grocery)
0.20
0.15
0.10 iALS
0.05
0.00 iCA baseline
0.00 0.05 0.10 0.15 0.20
Recall-precision (VoD) iTALS
0.08 (season)
0.06 iTALS
(sequence)
0.04
0.02
0.00
0.00 0.05 0.10 0.15 0.20

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. Thank you for your attention!
T