Talk deals with a practical recommendation sceaario - item-2-item recommendation (similar/related items) with implicit feedback and context. Solution is provided in the factorization framework. Paper abstract (to appear in ACM digital Library) Item-to-item recommendation - when the most similar items sought to the actual item - is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.

1. CONTEXT-AWARE SIMILARITIES
WITHIN THE FACTORIZATION
FRAMEWORK
Balázs Hidasi
Domonkos Tikk
CARR WORKSHOP, 5TH FEBRUARY 2013, ROME

2. OUTLINE
 Background & scope
 Levels of CA similarity
 Experiments
 Future work

3. BACKGROUND
Implicit feedback Context
I2I
Factorization
recommendation
This work

4. IMPLICIT FEEDBACK
 User preference not coded explicitely in data
 E.g. purchase history
 Presence  preference assumed
 Absence  ???
 Binary „preference” matrix
 Zeroes should be considered
 Optimize for
 Weighted RMSE
 Partial ordering (ranking)

5. CONTEXT
 Can mean anything
 Here: event context
 User U has an event
 on Item I
 while the context is C
 E.g.: time, weather, mood of U, freshness of I, etc.
 In the experiments:
 Seasonality (time of the day or time of the week)
 Time period: week / day

6. FACTORIZATION I
 Preference data can be organized into matrix
 Size of dimensions high
 Data is sparse
 Approximate this matrix by the product of two low
rank matrices
 Each item and user has a feature vector
 Predicted preference is the scalar product of the
item
appropriate vectors
user r
T
 r Uu Ii
u ,i
 Here we optimize for wRMSE (implicit case)
 Learning features with ALS

7. FACTORIZATION II.
 Context introduced  additional context dimension
 Matrix  tensor (table of records)
 Models for preference prediction:
item
 Elementwise product model
 Weighted scalar product user r
T
 ru , i , c 1 (U u  I i  C c )
 Pairwise model
 Context dependent user/item bias
T T T
 ru , i , c Uu Ii Uu Cc Ii Cc
context
context
item
+ +
user user item r

8. ITEM-TO-ITEM RECOMMENDATIONS
 Items similar to the current item
 E.g.: user cold start, related items, etc.
 Approaches: association rules, similarity between
item consumption vectors, etc.
 In the factorization framework:
 Similarity between the feature vectors
T
 Scalar product: s i , j Ii I j
T
Ii Ij
 Cosine similarity: s i , j
Ii 2
Ij
2

9. SCOPE OF THIS PROJECT
 Examination wether item similarities can be
improved
 using context-aware learning or prediction
 compared to the basic feature based solution
 Motivation:
 If factorization models are used anyways, it would be
good to use them for I2I recommendations as well
 Out of scope:
 Comparision with other approaches (e.g. association
rules)

10. CONTEXT-AWARE SIMILARITIES: LEVEL1
 The form of computing similarity remains
T
 si, j Ii Ij Ii
T
Ij
si, j
Ii 2
Ij
2
 Similarity is NOT CA
 Context-aware learning is used
 Assumption: item models will be more accurate
 Reasons: during the learning context is modelled separately
 Elementwise product model
 Context related effects coded in the context feature vector
 Pairwise model
 Context related biases removed

11. CONTEXT-AWARE SIMILARITIES: LEVEL2
 Incorporating the context features
 Elementwise product model
 Similarities reweighted by the context feature
 Assumption: will be sensitive to the quality of the
context
T
 si , j ,c
T
1 Ii  Cc  I j 1 Ii  Cc  I j
s i , j ,c
 Pairwise model Ii 2
Ij
2
 Context dependent promotions/demotions for the
participating items
 Assumption: minor improvements to the basic similarity
T T T
 s i , j ,c Ii Ij Ii Cc Ij Cc

12. CONTEXT-AWARE SIMILARITIES: LEVEL2
NORMALIZATION
 Normalization of context vector
 Only for cosine similarity
 Elementwise product model:
 Makes no difference in the ordering
 Recommendation in a given context to a given user
 Pairwise model
 Might affect results
 Controls the importance of item promotions/demotions
T T T
Ii Ij Ii Cc Ij Cc
s i , j ,c
Ii Ij Ii 2
Ij
2 2 2
T T T
Ii Ij Ii Cc Ij Cc
s i , j ,c
Ii Ij Ii 2
Cc 2
Ij Cc
2 2 2 2

13. EXPERIMENTS - SETUP
 Four implicit dataset
 LastFM 1K – music
 TV1, TV2 – IPTV
 Grocery – online grocery shopping
 Context: seasonality
 Both with manually and automatically determined time
bands
 Evaluation: recommend similar items to the users’
previous item
 Recall@20
 MAP@20
 Coverage@20

14. EXPERIMENTS - RESULTS
Improvement for Grocery Improvement for LastFM
35.00% 200.00% 183.91%
29.40%
30.00%
150.00%
25.00% 21.40%
20.00% Recall 100.00%
Recall
15.00% 12.90% Coverage
Coverage
10.00% 50.00%
6.11% 6.79% 6.14% 17.99%19.72%
5.14% 5.83%
3.91% 3.53% 5.27%
5.00%
0.87% 0.00%
0.00% -7.92% -1.62% -3.18%
-18.26% -21.96%
-50.00%
Improvement for TV1 Improvement for TV2
900.00% 797.51% 20.00%
800.00% 0.91% 0.79%
0.00% 0.59%
700.00%
-0.23% -3.23%
600.00% -20.00% -8.08% -8.13% -10.40%
500.00% Recall
Recall
400.00% -40.00%
Coverage -37.24% Coverage
300.00%
-60.00%
200.00%
7.34% 46.40%
100.00% 0.69% 8.31%
1.77% 6.77% 5.89% 4.86% -80.00%
0.00% -82.32%
-3.53% -100.00%
-100.00%
 From left to right: L1 elementwise, L1 pairwise, L2 elementwise, L2 pairwise, L2 pairwise (norm)

15. EXPERIMENTS - CONCLUSION
 Context awareness generally helps
 Impromevent greatly depends on method and
context quality
 All but the elementwise level2 method:
 Minor improvements
 Tolerant of context quality
 Elementwise product level2:
 Possibly huge improvements
 Or huge decrease in recommendations
 Depends on the context/problem

16. (POSSIBLE) FUTURE WORK
 Experimentation with different contexts
 Different similarity between feature vectors
 Predetermination wether context is useful for
 User bias
 Item bias
 Reweighting
 Predetermination of context quality
 Different evaluation methods
 E.g. recommend to session

17. THANKS FOR THE ATTENTION!
For more of my recommender systems related research visit my website:
http://www.hidasi.eu