Predicting Sequential Rating Predicting Sequential Rating

Elicited from Humans Elicited from Humans

Aviv Zohar & Eran Marom

2

MotivationRecommender systems base

recommendations on information collected from users

Humans are not consistent when rating itemsFraming problem (scaling)Relative ratings, not absoluteShort memory

Most recommender systems usually ignore the sequential aspect of the rating process

We wanted to find out if we can use this information to better predict unseen future ratings

3

Collected Data12 quotes of different qualities were

chosenEach user rates all quotes in a random

orderQuotes are easy and quick to rankA total of 500 people completed the surveyBinned the ratings to 5 values

4

Simple Analysis of the DataA naïve classifier that picks the most

common rating is right 33.8% of the time Ordered data seems to have added

information

5

The Graphical Model

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Dependencies in the Model

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Given the data, and not the quotes qualities, every path between types and qualities is open

When quotes qualities are given types are independent and vice versa

Conclusion: types are hard to learn together with qualities

The learning is exponential in min(NPeople,NQuotes)

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Likelihood of the Data

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EM - Learning Parameters(known types)

Expectation

Maximization

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Selecting Strict People Types Cleverly

Using additional information on usersClustering using ratings

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How to Predict the Next Rating

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Unknown, can be estimated from previous ratings

We have a prior from past data

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How to Predict the Next Rating

Use the previous ratings by the user to estimate the user type, and then predict the following rating

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ExperimentsLearning Process:

Initialized with random, almost uniform, priors

Final network selected by max likelihood from 100 EM runs

Score: percentage of correct predictions5-fold cross validation was used

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Results for Learning with Strictly Selected People

TypesTo check if the order information helps us

with prediction, tests were repeated with scrambled order

Only a slight difference was foundTrue order – 37.98%Permuted order – 37.40%

The type of each person is better estimated with each new rating given –so we’ll make less mistakes over time

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Adaptively Assigning People Types

Multi-phase EMAlternating between optimal selection of

types and optimal selection of parameters

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Results for Learning with Dynamic Adaptation of

People TypesAverage score is still 0.5% better for the

ordered runCould not see improvements in predictions

compared to constant people types deduced from k-means

General rise in score with progress of ratings is seen, though other underlying aspects of the data have effect

PermutatedOrdered

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Numerical Problems when Increasing the Data Size

Insufficient data for learning the parameter space11 rating values, 3 quote types, 2 person

typesNumber of parameters to learn:

1112323795Number of predictions from 500 people:

500126000Precision problems

Alas, using more than 400 people implies such small probabilities that we have to use infinite precision tools

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Conclusions and Possible Extensions

A significant improvement is already gained by very naïve predictors, but still we can do better

Adding order dependencies improves predictions only slightly

Multi-phase learning could be done using strict quotes qualities while using types probabilities for each person

Model can be extended to encompass more aspects of sequential information

More data may yield better resultsAlgorithms ran fast – system is scalable

to larger data sets