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We present a probabilistic group recommendation model. And, also, a framework (alternative to Matrix Factorisation and Neighbourhood methods) that can be used to build personalised search, recommendation, people match, ad relevance matching models without reducing the dimensionality or computing explicit similarity.
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Probabilistic Group Recommendationvia Information Matching
Jagadeesh Gorla (@jgorla)1 Neal Lathia (@neal lathia)2 Stephen Robertson3 Jun Wang (@seawan)1
1University College London
2University of Cambridge
3Microsoft Research Cambridge
What is the problem?• Group recommendation
• How to computePr(group relevance | group, activity)?
• A probabilistic group recommendation model!
What is the problem?
• Group recommendation
• Individual users preferences?• Type of the group (group preferences)?
Type of the groups
• Consensus preferences group
• Relevant to every group member
Type of the groups
• Shared preferences group
• Relevant to every group member, or at-least notdisliked by majority of the group members
Type of the groups
• Split preferences group
• Relevant to at-least one group member• e.g., Group of household members sharing the same
TV but consume at different times
Individual vs. Group preferences
• Individual preferences
Individual vs. Group preferences
• Individual preferences
Individual vs. Group preferences
What if they decide to watch a movie together?
Group recommendations?
Merging individual preferences• Merge and create group profile• Generate recommendations for group
Problem: May present unwanted items, e.g.,Spartacus
Merging individual recommendations• Compute a list of recommendations for each member• Merge the individual lists
Problem: May lose preferences as part of a group
Group recommendations?
Merging individual preferences• Merge and create group profile• Generate recommendations for group
Problem: May present unwanted items, e.g.,Spartacus
Merging individual recommendations• Compute a list of recommendations for each member• Merge the individual lists
Problem: May lose preferences as part of a group
Group recommendations?
Merging individual preferences• Merge and create group profile• Generate recommendations for group
Problem: May present unwanted items, e.g.,Spartacus
Merging individual recommendations• Compute a list of recommendations for each member• Merge the individual lists
Problem: May lose preferences as part of a group
Group recommendations?
Merging individual preferences• Merge and create group profile• Generate recommendations for group
Problem: May present unwanted items, e.g.,Spartacus
Merging individual recommendations• Compute a list of recommendations for each member• Merge the individual lists
Problem: May lose preferences as part of a group
Group recommendations?
Individual preference in a group may vary
Group recommendation should consider,• Individual preferences• Group preferences
Hypothesis,• Group relevance is a function of “individual group
member preferences”
Group recommendations?
Individual preference in a group may vary
Group recommendation should consider,• Individual preferences• Group preferences
Hypothesis,• Group relevance is a function of “individual group
member preferences”
Group recommendations?
Individual preference in a group may vary
Group recommendation should consider,• Individual preferences• Group preferences
Hypothesis,• Group relevance is a function of “individual group
member preferences”
Probabilistic model
Some notation:1 G is a set of users ({u1, u2 · · · , uh})2 Rg = 1 if the item is relevant to the group, and 0
otherwise3 < is a binary vector of individual relevance
Probabilistic model• Group relevance
P (Rg = 1|G, i) ∝Rg∑<
∏hj=1 P (Rj, uj, i|Rg = 1) ×
∏hj=1 P (Rj|uj, i)
• Individual relevance
• Least misery strategy:
P (Rg = 1|G, i) ∝Rg
min{P (R1 = 1|u1, i), · · · , P (Rh = 1|uh, i)}
Probabilistic model• Group relevance
P (Rg = 1|G, i) ∝Rg∑<
∏hj=1 P (Rj, uj, i|Rg = 1) ×
∏hj=1 P (Rj|uj, i)
• Individual relevance
• Least misery strategy:
P (Rg = 1|G, i) ∝Rg
min{P (R1 = 1|u1, i), · · · , P (Rh = 1|uh, i)}
Probabilistic model• Group relevance
P (Rg = 1|G, i) ∝Rg∑<
∏hj=1 P (Rj, uj, i|Rg = 1) ×
∏hj=1 P (Rj|uj, i)
• Individual relevance
• Least misery strategy:
P (Rg = 1|G, i) ∝Rg
min{P (R1 = 1|u1, i), · · · , P (Rh = 1|uh, i)}
Probabilistic model• Group relevance
P (Rg = 1|G, i) ∝Rg∑<
∏hj=1 P (Rj, uj, i|Rg = 1) ×
∏hj=1 P (Rj|uj, i)
• Individual relevance
• Least misery strategy:
P (Rg = 1|G, i) ∝Rg
min{P (R1 = 1|u1, i), · · · , P (Rh = 1|uh, i)}
Relevance to an individual
Name: Jane SmithSex: FemaleAge: 27Location: Ipanema
Product: ShoeType: FormalBrand: ChanelColour: Red
How to compute the relevance between Jane (“girl fromIpanema”) & Shoe?
Relevance to an individual
Name: Jane SmithSex: FemaleAge: 27Location: Ipanema
Product: ShoeType: FormalBrand: ChanelColour: Red
How to compute the relevance between Jane (“girl fromIpanema”) & Shoe?
Relevance to an individual
Traditional approaches:• Neighbourhood approaches
• Assume common feature space• matrix factorisation (e.g., PureSVD)
• Model features as a user/item
Relevance to an individual
Traditional approaches:• Neighbourhood approaches• Assume common feature space
• matrix factorisation (e.g., PureSVD)
• Model features as a user/item
Relevance to an individual
Traditional approaches:• Neighbourhood approaches• Assume common feature space
• matrix factorisation (e.g., PureSVD)
• Model features as a user/item
Relevance to an individual
We want a framework with:• No explicit similarity
• No common feature space• Interpretable features
Information Matching Model (IMM)or
Bi-directional Unified Model
Relevance to an individual
We want a framework with:• No explicit similarity• No common feature space
• Interpretable features
Information Matching Model (IMM)or
Bi-directional Unified Model
Relevance to an individual
We want a framework with:• No explicit similarity• No common feature space• Interpretable features
Information Matching Model (IMM)or
Bi-directional Unified Model
Relevance to an individual
We want a framework with:• No explicit similarity• No common feature space• Interpretable features
Information Matching Model (IMM)or
Bi-directional Unified Model
Idea ...
• Find a best match for Me
• man −−−−→ woman︸ ︷︷ ︸man preferences
+ man←−−−−woman︸ ︷︷ ︸woman preferences
Idea ...
• Find a best match for Me
• man −−−−→ woman︸ ︷︷ ︸man preferences
+ man←−−−−woman︸ ︷︷ ︸woman preferences
Idea ...
• Find a best match for Me
• man −−−−→ woman︸ ︷︷ ︸man preferences
+ man←−−−−woman︸ ︷︷ ︸woman preferences
IMM
U/Q/P α3
α2
α4
α1
. . .
αl
β3
β2
β4
β1
. . .
βk
Pro/D/P/Ad
1
IMM
U/Q/P α3
α2
α4
α1
. . .
αl
β3
β2
β4
β1
. . .
βk
Pro/D/P/Ad
1
IMM
U/Q/P α3
α2
α4
α1
. . .
αl
β3
β2
β4
β1
. . .
βk
Pro/D/P/Ad
1
IMM
U1
U2
. . .
α3
α2
α4
α1
. . .
αl
β3
β2
β4
β1
. . .
βk
Pr1
Pr2
. . .
1
It solves the problem of “Unified Model for InformationRetrieval”
S.E. Robertson, M.E. Maron and W.S. Cooper, Theunified probabilistic model for IR, 1982.
Data
Dataset Users Movies Ratings scaleMovieLens1 1K 1.7K 100K [1-5]MovieLens2 6K 4K 1M [1-5]MoviePilot (Tr) 171K 24K 4.4M [0-100]MoviePilot (Eva) 594 811 4,482 [0-100]
Number of households: 290
Evaluation Methodology
Evaluation:• Individual recommendation• Household recommendation
Individual recommendation• Randomly divide the data (60% training and 40%
testing) – Movie Lens• Rank all the items• Precision@N, NDCG@N and Mean Average
Precision (MAP)
Individual recommendation
Figure: Recommending to Individuals.
Performance Loss
Figure: PureSVD Figure: IMM
Conclusion
• Can develop powerful group recommendation modelswithin the framework
• Take advantage of probabilistic modelling• Individual recommendation is crucial for group
recommendation• Information Matching Model (IMM) framework can
be used to build:• Search• Job matching• People matching (e.g., dating)• Product recommendation (ads, retail, etc.)• Targeted marketing
Thank You & Questions
Acknowledgements:
• This work has been sponsored by• My personal thanks to Ulrich Paquet ( )
Graphical Model
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xi yj
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θvi γvj
gij hji
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