[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on LinkedIn
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[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on LinkedIn






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[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on LinkedIn [RecSys '13]Pairwise Learning: Experiments with Community Recommendation on LinkedIn Presentation Transcript

  • Pairwise Learning: Experiments with Community Recommendation on LinkedIn Amit Sharma*, Baoshi Yan asharma@cs.cornell.edu, byan@linkedin.com
  • Typical online recommendation interfaces
  • Community Recommendation on LinkedIn Observed preference user u joins a community y (u,y) The recommendation problem Given a set of (u, y) tuples, predict a set R(u) for each user which are the recommendations for a user u. A content-based approach Owing to the rich profile data for users, we use a contentbased model that computes similarity between users and groups.
  • An intuitive logistic model (pointwise) fu, fy: features of user u and community y wi : parameters for the model Communities that a user has joined are relevant.
  • Understanding implicit feedback from users 1 2 3 4 5 Clicked 2 is better than 1.
  • 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] Caveat Learning time is quadratic in number of communities. How fast is the inference?
  • 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
  • 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:
  • Building a pairwise logistic recommender Maximizing the likelihood of observed preference among pairs:
  • 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
  • Model 2: Logistic Loss Model Assuming a more general ranking function: Ranking: As long as we choose h to be a nondecreasing function, we can still rank by computing weighted sum of features for each community.
  • Pairwise learning improves the classification of pairs Task: For each pair, predict which community is more preferred by a user ...but the gains are only slight.
  • Digging deeper: Joining statistics for LinkedIn communities Random sample, 1M users FACT: Most users join different types of groups. Possible hypothesis: There are different reasons for joining different types of groups.
  • Digging deeper: the effect of group types PREFERRED ML Group Interest Feature > User1 Cornell Alumni Education Feature PREFERRED Cornell Alumni Education Feature > User2 ML Group Interest Feature When learning a single weight for each feature, varying preferences of users may cancel out the effects.
  • Different reasons for joining a community can be treated as a set of latent preferences within a user Pair of communities User Core preference
  • 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.
  • Latent preferences over pairs help retain differing user preferences ML Group Interest Feature > User1 Cornell Alumni Education Feature Cornell Alumni z1 Education Feature > User2 ML Group Interest Feature User1 puts more weight to z1’s preference. User2 puts more weight to z2’s preference. z2
  • Some details about the model Number of core preferences (Z) small ~ {2, 4, 8} Choosing probability models Use logistic loss or feature difference for modeling conditional preference. Multinomial model for modeling the probability of a latent preference given a user.
  • Ranking Thus, we can still rank communities individually (without constructing pairs).
  • Evaluation Offline evaluation: Evaluated on group join data on linkedin.com during the summer of 2012. Train-test data separated chronologically.
  • Pairwise PLSI performs improves performance on learning pairwise preference
  • Pairwise PLSI leads to more successful recommendations
  • 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%.
  • 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