RecSys ’17: Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations
Recommendation system paper challenge (13/50)
What problem do they solve?
- Predicted item ration by considering the fact historical rating affect user rating bahavior
What model do they propose?
HIALF: Historical Influence Aware Latent Factor Model (HIALF)
b_u and q_up is traditional latent variable model
a_u is meaning how easy the user u to be affected.
h_pi represents the distortion from historical rating
|H_pi| represents the size of historical ratings, which is helpful for how affect the users. Imagine that if you saw 2 item with same rating 5/5 but 1 item with 10,000 ratings and the other one with 10 ratings. People prefer the first one. In this case, they take f(*) function to represent the effect.
β(x) is a categorical function to represent the induced bias when the difference between e_pi and q_up, they applied kernel function for it
Modeling prior expectation e_pi:
they applied 2 strategies:
- average of historical mean
- weighed more on more recent data
For learning:
With the objective functions with regularization terms, they applied SGD (stochastic gradient descent) to update the parameters.
Data
Comparing models
latent factor (LF) model: traditional model
HEARD (Quantifying Herding Effects in Crowd Wisdom): it is a model with considering social influence of prior collective opinions.
HIALF: their proposal model
HIALF-AVG: prior expectation is taken as the average of historical ratings without emphasis on recent ratings.
Result
Other related blogs:
Beyond Clicks: Dwell Time for Personalization
RecSys’15: Context-Aware Event Recommendation in Event-based Social Networks
RecSys16: Adaptive, Personalized Diversity for Visual Discovery
RecSys ’16: Local Item-Item Models for Top-N Recommendation
COLING’14: Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
NAACL’19: Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
RecSys ’17: Translation-based Recommendation
Best paper in RecSys:
https://recsys.acm.org/best-papers/
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