[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on LinkedIn
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Pairwise Learning:Experiments with Community Recommendation on LinkedIn
Amit Sharma*, Baoshi [email protected], [email protected]
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Typical online recommendation interfaces
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Community Recommendation on LinkedIn
Observed preferenceuser u joins a community y (u,y)
The recommendation problemGiven a set of (u, y) tuples, predict a set R(u) for eachuser which are the recommendations for a user u.
A content-based approachOwing to the rich profile data for users, we use a content-based model that computes similarity between users and groups.
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An intuitive logistic model (point-wise)
fu, fy: features of user u and community ywi : parameters for the modelCommunities that a user has joined are relevant.
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Understanding implicit feedback from users
1
32 Clicked
2 is better than 1.
45
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Can pairwise learning help for community recommendation?● A reliable technique used in search engines. [Joachims
01]
● Has been proposed for some collaborative filtering models. [Rendle et al. 09, Pessiot et al. 07]
● Empirical evidence shows promising results. [Balakrishnan and Chopra 10]
CaveatLearning time is quadratic in number of communities.How fast is the inference?
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Outline
● Propose pairwise models for content-based recommendation
● Augment pairwise learning with a latent preference model
● Show both offline and online evaluation on linkedin data for our proposed models
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Expressing pairwise preference
We establish a pair (yi, yj) if yi was ranked higher than yj and only yj was selected by the user.
We can define a ranking function h such that:
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Building a pairwise logistic recommender
Maximizing the likelihood of observed preference among pairs:
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Model 1: Feature Difference Model
Assuming h to be a linear function,
Equivalent to logistic classification with features(yj - yi)
Ranking: Can simply rank by computing for each community
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Model 2: Logistic Loss Model
Assuming a more general ranking function:
Ranking: As long as we choose h to be a non-decreasing function, we can still rank by computing weighted sum of features for each community.
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Pairwise learning improves the classification of pairs
...but the gains are only slight.
Task: For each pair, predict which community is more preferred by a user
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Digging deeper: Joining statistics for LinkedIn communities
FACT: Most users join different types of groups.
Possible hypothesis: There are different reasons for joining different types of groups.
Random sample, 1M users
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Digging deeper: the effect of group types
Cornell Alumni
ML Group
Cornell Alumni
ML Group
User1
User2
Interest Feature
Education Feature
Interest Feature
Education Feature
>
>
PREFERRED
PREFERRED
When learning a single weight for each feature, varying preferences of users may cancel out the effects.
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Different reasons for joining a community can be treated as a set of latent preferences within a user
Core preference
User
Pair of communities
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Model 3: Pairwise PLSI model
Extend the Probabilistic Latent Semantic Indexing recommendation model for pairwise learning [Hofmann 02]
We assume users are composed of a set of latent preferences. Each user differs in how she combines the available latent preferences.
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Latent preferences over pairs help retain differing user preferences
Cornell Alumni
ML Group
Cornell Alumni
ML Group
User1
User2
Interest Feature
Education Feature
Interest Feature
Education Feature
>
>
z1
z2
User1 puts more weight to z1’s preference. User2 puts more weight to z2’s preference.
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Number of core preferences (Z)small ~ {2, 4, 8}Choosing probability modelsUse logistic loss or feature difference for modeling conditional preference.
Multinomial model for modeling the probability of a latent preference given a user.
Some details about the model
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Ranking
Thus, we can still rank communities individually (without constructing pairs).
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Evaluation
Offline evaluation: Evaluated on group join data on linkedin.com during the summer of 2012.
Train-test data separated chronologically.
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Pairwise PLSI performs improves performance on learning pairwise preference
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Pairwise PLSI leads to more successful recommendations
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Online evaluation
● Tested out Logistic Loss and Feature Difference models on 5% of LinkedIn users, and the baseline model on the rest
● Measured average click-through-rate (CTR) over 2 weeks
● Feature difference reported a 5% increase in CTR, logistic loss reported 3%.
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Conclusion: Pairwise learning can be a useful addition.
However, gains may depend on the context / domain.Important to understand and model the special characteristics of a target domain.
thank you Amit Sharma, @amt_shrma
www.cs.cornell.edu/~asharma