This document proposes algorithms to improve music recommendation in session-based collaborative filtering by incorporating temporal context. It summarizes that previous approaches did not use temporal features extracted from listening sessions. The paper presents temporal context-aware algorithms that cluster sessions based on temporal features like time of day, and represent sessions as mixtures of topics using LDA. An evaluation on a dataset of over 19 million listening records found the LDA-based approach significantly outperformed traditional session-based CF, increasing hit ratio by over 200%. Future work could combine temporal properties with other context features and conduct a user study.

1. Improving Music Recommendation in
Session-Based Collaborative Filtering by using
Temporal Context
Ricardo Dias, Manuel J. Fonseca
University of Lisbon
Instituto Superior Técnico
ICTAI 2013

3. Amazon
17 Million Songs
iTunes
~18 Million Songs

6. Content information
User Feedback
Songs listened
Temporal Context

7. Context Filtering
Collaborative Filtering
Demographic Filtering
Content-Based Approaches
Hybrid Approaches
[Celma2010]

8. 1. Few work to study the usage of temporal
context combined with CF algorithms
2. Some take advantage of the songs listened in
sessions (and implicit context they provide)
3. None of them used temporal features
extracted from the sessions

9. 1. Few work to study the usage of temporal
context combined with CF algorithms
2. Some take advantage of the songs listened in
sessions (and implicit context they provide)
3. None of them used temporal features
extracted from the sessions

10. 1. Few work to study the usage of temporal
context combined with CF algorithms
2. Some take advantage of the songs listened in
sessions (and implicit context they provide)
3. None of them used temporal features
extracted from the sessions

12. Listening to music is a repetitive
and continuous process
[Herrera2010]

13. Songs frequently preferred together in sessions
rather than isolated
[Hansen2009]

14. User 1
Session 1
Session 2
…
User 2 …
Artist 1, Song 1, Timestamp
Artist 2, Song 3, Timestamp
Artist 1, Song 2, Timestamp
Artist 3, Song 5, Timestamp
Artist 4, Song 4, Timestamp
Time

15. COLLABORATIVE FILTERING
APPROACHES

16. Takes into account the songs a user has listened
and rated
Song 1
Song 2
Song 3
Song 4
Song 5
Alice
5
3
4
4
?
User 1
3
1
2
3
3
User 2
4
3
4
3
5
User 3
3
3
1
5
4
User 4
1
5
5
2
1

18. Session profiles instead of User Profiles
Song 1
Song 2
Session 1
1
4
Session 2
1
Session 3
[Park2011]
Song 4
Song 5
4
?
2
1
Session 4
Session 5
Song 3
2
1
1
2
1
1
1

19. Song 1
Song 2
Session 1
1
4
Session 2
1
Session 3
Song 4
Song 5
4
?
2
1
Session 4
Session 5
Song 3
2
1
1
2
1
1
1

20. 𝑟 𝑠,𝑖 = 𝑟𝑠 +
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟 𝑣,𝑖 − 𝑟 𝑣 )
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎(𝒔, 𝒗)
𝑺(𝒔) 𝒌 represents the k session neighbors for the active sessions, 𝒓 𝒔 and 𝒓 𝒗 the average
rating for the sessions s and v under comparison

21. TEMPORAL CONTEXT AWARE
ALGORITHMS

22. Explicit
Implicit

23. 𝑟 𝑠,𝑖 = 𝑟𝑠 +
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟 𝑣,𝑖 − 𝑟 𝑣 )
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎(𝒔, 𝒗)
𝑺(𝒔) 𝒌 represents the k session neighbors for the active sessions, 𝒓 𝒔 and 𝒓 𝒗 the average
rating for the sessions s and v under comparison

24. 𝑟 𝑠,𝑖 = 𝑟𝑠 +
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟 𝑣,𝑖 − 𝑟 𝑣 )
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎(𝒔, 𝒗)
𝑺(𝒔) 𝒌 represents the k session neighbors for the active sessions, 𝒓 𝒔 and 𝒓 𝒗 the average
rating for the sessions s and v under comparison

25. Explicit

26. Features
Time (Period of Day)
Temporal
Context
Weekday
Day of Month
Month
Session Diversity
Session
Diversity

27. Clustering performed to group sessions that
have similar features
Expectation Maximization (EM)
[Dempster1977]

28. Cluster
Membership
S1 and S2 more similar than S1 and S3 or S2 and
S3
1
0,8
0,6
Session 1
0,4
0,2
Session 2
0
Session 3
1
2
3
Clusters
4
sim(s1, s2) = 0.163
sim(s1, s3) = 1.139
sim(s2, s3) = 0.871

29. 𝑟 𝑠,𝑖 = 𝑟𝑠 +
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟 𝑣,𝑖 − 𝑟 𝑣 )
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎(𝒔, 𝒗)
𝒔𝒊𝒎 𝒔, 𝒗 = 𝑲𝒖𝒍𝒍𝒃𝒂𝒄𝒌(𝒔, 𝒗)

30. Implicit

31. Documents are collections of words
Each document is represented as a mixture of
latent topics, and each topic has probabilities of
generating various words

32. Uses Session Data Model with LDA
– Documents: sessions
– Words: songs
Sessions (Documents) are treated as Bag-ofSongs (Words)

33. Topic
Distribution
S1 and S2 more similar than S1 and S3 or S2 and
S3
1
0,8
0,6
0,4
Session 1
0,2
Session 2
Session 3
0
1
2
3
Topics
4
sim(s1, s2) = 0.163
sim(s1, s3) = 1.139
sim(s2, s3) = 0.871

34. 𝑟 𝑠,𝑖 = 𝑟𝑠 +
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟 𝑣,𝑖 − 𝑟 𝑣 )
𝑣 ∈ 𝑆(𝑠) 𝑘
𝒔𝒊𝒎(𝒔, 𝒗)
𝒔𝒊𝒎 𝒔, 𝒗 = 𝑲𝒖𝒍𝒍𝒃𝒂𝒄𝒌(𝒔, 𝒗)

35. EVALUATION SETUP

36. Determine the next song to be played in active
session
Song E
Song A
Song B
Song C
Active Session
Song D
Song F

37. Listening History of 992 users
> 19
Million records

38. Session 1
Song 1
Song 2
Session 2
Song 3
Song 4
Time gap
[Pabarskaite2007]
Song 6
Song 3

39. Temporal Context Aware
SSCF [Park2011]
TSSCF
Session LDA

40. #c = 8

41. #t = 8

42. 𝑘 ∈ 30, 50, … , 2000
[Park2011]

43. th = 20

44. HR@n  Hit Ratio at N
(top-n recommendations)
MRR  Mean Reciprocal Rank

45. Dataset – Session split
Training: all the songs in sessions, except the last
one
Test: the last song in the session

46. Queries  1000 Sessions randomly
chosen
For i = 1
to i = 20
For each query obtained the top-100,
and recorded the presence and rank
given by the algorithms

47. RESULTS

48. SSCF
Hit Ratio
MRR
LDA

49. Hit Ratio
H(2) = 1560.115; P < 0.01
MRR
H(2) = 1064.659; P < 0.01

50. Hit Ratio
MRR

51. Hit Ratio
MRR

52. K = 30

53. Hit Ratio
MRR
LDA
LDA

54. Hit Ratio
H(2) = 52.52; P < 0.01
MRR
H(2) = 2.294; P > 0.318

55. CONCLUSIONS AND FUTURE
WORK

56. Temporal Context improves recommendation
accuracy in Session-based CF
– LDA based algorithm with best results
HR increased >200%

57. Combine temporal properties with other
context features
– Example: locations, activities, user behavior
(skips), etc.
Conduct a user study by applying the algorithm
in a real world application