online evolutionary collaborative filtering recsys 2010 intelligent database systems lab. school of...
DESCRIPTION
Copyright 2010 by CEBT Introduction User’s preference changes over a long period of time Online evolutionary collaborative filtering Tracks user interests over time in order to make timely recommendations Extension of neighborhood based algorithms 3 user1 user2 PopPop JazzJazzClassic PopPop JazzJazzClassicTRANSCRIPT
Online Evolutionary Collaborative Filtering
RECSYS 2010
Intelligent Database Systems Lab.School of Computer Science & Engineering
Seoul National UniversityCenter for E-Business TechnologySeoul National UniversitySeoul, Korea
Presented by Sung Eun, Park3/25/2011
Nathan N. Liu, Min Zhao, Evan Xiang, Qiang YangHong Kong University of Science and Technology, Hong Kong,
Copyright 2010 by CEBT
Contents
Introduction Evolutionary Collaborative Filtering Online evolutionary Collaborative Filtering
Incremental Similarity Computation Experiments Conclusion
2
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Introduction
User’s preference changes over a long period of time Online evolutionary collaborative filtering
Tracks user interests over time in order to make timely recommendations
Extension of neighborhood based algorithms
3
user1
user2
Pop Pop Jazz Jazz Classic
PopPop JazzJazzClassic
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Typical Item-based Collaborative Filtering
1. Similarity Computation: Compute the item-item similarities (Cosine Similarity)
2. Neighborhood Computation: Find the most similar k-items
3. Score Prediction: Predict the unobserved ratings
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For all users who rated both i and j
For all similar items
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Evolutionary Collaborative Filtering
Temporal Relevance Weight of rating on item i of user u at time t on parameter
α Should decrease with the amount of time that has passed Based on the assumption that older ratings are generally
less correlated with a user’s current interests or an item’s current characteristic
A time gab between the current time and the time user rated the item i
Emphasizing currently rated items
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Evolutionary Collaborative Filtering
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Evolutionary Collaborative Filtering
Similarity Computation More emphasis on the recent rating of both items Inclined to identify nearest neighbors
user1
user2
user3
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Evolutionary Collaborative Filtering
user3
user3
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Incremental Similarity Computation The problem of efficiently updating the model as new data
arrives over time in large volumes
Online Evolutionary Collaborative Filtering
where
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Online Evolutionary Collaborative Filtering
Incremental Similarity Computation A set of users who newly rated i in time step t
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Online Evolutionary Collaborative Filtering
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Experiments
Dataset Analysis Early ratings were often
high – probably because they are
most voted by the most enthusiastic fans
Slow increase over time– Very old movies that has
watched were often classics and therefore more likely to receive high ratings
The variance of users’ ratings tended to increase over time– Better to user old age
users to catch a explicit preference
Movie age
User age
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Dataset Analysis
A user tended to rate many more movies when he joined and became less and less active over time
Experiments
Movie age
user age
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Evaluation Measure
RMSE(Root mean square error) The rating prediction accuracy How close their predicted ratings are to the true ratings
MAP(Mean Average Precision) Choice Prediction
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Evaluation Results
Effect of temporal relevance weighting
RMSE MAP
1. In the item-based algorithm, the predicted scores are obtained by averaging a target user’s very few observed ratings 2. temporal relevance weighting’s effect to further reduce the contribution of old ratings would make the prediction less robust
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Evaluation Results
Effect of temporal relevance weighting
Why are improvements on new items important?1. the cosine similarity tends to favor old movies
Old movies get more ratings and their cosine similarity with other movies tend to be higher
2. Reflects users’ current interests
RMSE
Movie Age Group User Age Group
Better at new items
Better at old users
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Evaluation Results
Effect of temporal relevance weighting
Better at old users Consistent with the intuition that it is more likely for the
taste of old users to have drifted over time
MAP
Movie Age Group User Age Group
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Evaluation Results
Effect of incremental computation Incremental algorithm is 15 -20 times faster than the non
incremental algorithm
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Conclusion
The use of temporal relevance weighting could lead to more significant improvements for the choice prediction task than for the rating prediction task.
A detailed analysis reveals that our algorithm can most effectively improve predictions for older users and newer items.
The proposed algorithm is simple and fast enough to cope with frequent data updates
Q&A
Thank you
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