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Temporal Context
Improving Music Recommendation in Session-Based Collaborative Filtering by using
Ricardo Dias, Manuel J. Fonseca
University of Lisbon
Instituto Superior Técnico
ICTAI 2013
Amazon
17 Million Songs iTunes
~18 Million Songs
Content information
User Feedback
Songs listened
Temporal Context
Context Filtering
Collaborative Filtering Demographic Filtering
Content-Based Approaches
Hybrid Approaches [Celma2010]
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
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
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
Listening to music is a repetitive and continuous process
[Herrera2010]
Songs frequently preferred together in sessions rather than isolated
[Hansen2009]
User 1 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
…
User 2 …
Time
Session 1
Session 2
COLLABORATIVE FILTERING APPROACHES
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
Session profiles instead of User Profiles
[Park2011]
Song 1 Song 2 Song 3 Song 4 Song 5
Session 1 1 4 4 ?
Session 2 1 2
Session 3 1 2 1
Session 4 1 1
Session 5 2 1 1
Song 1 Song 2 Song 3 Song 4 Song 5
Session 1 1 4 4 ?
Session 2 1 2
Session 3 1 2 1
Session 4 1 1
Session 5 2 1 1
𝑟 𝑠,𝑖 = 𝑟 𝑠 + 𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟𝑣,𝑖 − 𝑟 𝑣)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎(𝒔, 𝒗)𝑣 ∈ 𝑆(𝑠)𝑘
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
TEMPORAL CONTEXT AWARE ALGORITHMS
Implicit Explicit
𝑟 𝑠,𝑖 = 𝑟 𝑠 + 𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟𝑣,𝑖 − 𝑟 𝑣)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎(𝒔, 𝒗)𝑣 ∈ 𝑆(𝑠)𝑘
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
𝑟 𝑠,𝑖 = 𝑟 𝑠 + 𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟𝑣,𝑖 − 𝑟 𝑣)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎(𝒔, 𝒗)𝑣 ∈ 𝑆(𝑠)𝑘
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
Explicit
Features
Time (Period of Day)
Weekday
Day of Month
Month
Session Diversity
Temporal
Context
Session
Diversity
Clustering performed to group sessions that have similar features
Expectation Maximization (EM)
[Dempster1977]
S1 and S2 more similar than S1 and S3 or S2 and S3
sim(s1, s2) = 0.163
sim(s1, s3) = 1.139
sim(s2, s3) = 0.871
0
0,2
0,4
0,6
0,8
1
1 2 3 4
Clu
ste
r M
em
be
rsh
ip
Clusters
Session 1
Session 2
Session 3
𝑟 𝑠,𝑖 = 𝑟 𝑠 + 𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟𝑣,𝑖 − 𝑟 𝑣)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎(𝒔, 𝒗)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎 𝒔, 𝒗 = 𝑲𝒖𝒍𝒍𝒃𝒂𝒄𝒌(𝒔, 𝒗)
Implicit
Documents are collections of words
Each document is represented as a mixture of latent topics, and each topic has probabilities of generating various words
Uses Session Data Model with LDA
– Documents: sessions
– Words: songs
Sessions (Documents) are treated as Bag-of-Songs (Words)
S1 and S2 more similar than S1 and S3 or S2 and S3
sim(s1, s2) = 0.163
sim(s1, s3) = 1.139
sim(s2, s3) = 0.871
0
0,2
0,4
0,6
0,8
1
1 2 3 4
Top
ic
Dis
trib
uti
on
Topics
Session 1
Session 2
Session 3
𝑟 𝑠,𝑖 = 𝑟 𝑠 + 𝒔𝒊𝒎 𝒔, 𝒗 ∙ (𝑟𝑣,𝑖 − 𝑟 𝑣)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎(𝒔, 𝒗)𝑣 ∈ 𝑆(𝑠)𝑘
𝒔𝒊𝒎 𝒔, 𝒗 = 𝑲𝒖𝒍𝒍𝒃𝒂𝒄𝒌(𝒔, 𝒗)
EVALUATION SETUP
Determine the next song to be played in active session
Song A Song B Song C
Song E
Song D
Song F Active Session
Listening History of 992 users
> 19 Million records
Song 1 Song 2 Song 3 Song 4 Song 6 Song 3
Session 1 Session 2
Time gap
[Pabarskaite2007]
Temporal Context Aware
SSCF [Park2011]
TSSCF
Session LDA
#c = 8
#t = 8
𝑘 ∈ 30, 50,… , 2000
[Park2011]
th = 20
HR@n Hit Ratio at N
(top-n recommendations)
MRR Mean Reciprocal Rank
Dataset – Session split
Training: all the songs in sessions, except the last one
Test: the last song in the session
Queries 1000 Sessions randomly chosen
For each query obtained the top-100, and recorded the presence and rank given by the algorithms
For i = 1
to i = 20
RESULTS
LDA
SSCF Hit Ratio MRR
H(2) = 1560.115; P < 0.01 H(2) = 1064.659; P < 0.01
Hit Ratio MRR
Hit Ratio MRR
Hit Ratio MRR
K = 30
MRR
LDA LDA
Hit Ratio
H(2) = 2.294; P > 0.318 H(2) = 52.52; P < 0.01
Hit Ratio MRR
CONCLUSIONS AND FUTURE WORK
Temporal Context improves recommendation accuracy in Session-based CF
– LDA based algorithm with best results
HR increased >200%
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
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