Active Learning for Preferences Elicitation in Recommender Systems
Lior Rokach
Department of Information System Engineering
:
Active Learning for Preferences Elicitation in Recommender Systems
Lior Rokach
Department of Information System Engineering
:
Agenda Background - Active Learning and Recommender Systems
Proposed Method
Experimental Procedure
Results and Discussion
Conclusions and Future Work
Recommender Systems Users are overloaded by options to consider before making a decision
such as item to purchase
Recommender systems aim at supporting the user in the processes of
decision-making
planning
purchasing
Collaborative Filtering Maintain users’ ratings of a variety of items.
For a given user:
Find other similar users whose ratings strongly correlate with the current user
Recommend items rated highly by these similar users, but not rated by the current user.
Almost all existing commercial recommenders use this approach (e.g. Amazon).
Collaborative Filtering
Active Learning Traditional supervised learning algorithms
passively accept labeled training data and induce prediction model
Active learning
useful when unlabeled data is abundant
labels are expensive
allows intelligent selection of which examples to label. Passive Learning Active Learning
Using Active Learning for Initial Preferences Elicitation The cold start problem
very little is known about the preferences of new users
Possible modus operandi
Ask the user to rate a few items
Which items ? Active Learning
Using Active Learning for Initial Preferences Elicitation Active Learning Active Learning
Active Learning in Critique-Based Recommender Systems (Ricci and Nguyen, 2007) A series of interaction cycles to
narrow down the user’s query
until the desired item is obtained
Integrating Active Learning in CF-based Recommender Systems Active Learning (AL) in RecSys
accurately predicts items of interest to the user
while gaining information about her preferences.
In this lecture we focus on
Uncertainty Active Collaborative Filtering
Boutilier et al. (2003)
Rong and Luo (2004)
…
Incorporate exploration and exploitation trade-off.
Work local – think global
Use the ratings of one user to contribute to other users
Introduce Cost-Sensitivity (Not going to talk about that) Our Contributions the value of information of new ratings the alternative utility for not presenting the best items VS
Agenda Background - Active Learning and Recommender Systems
Proposed Method
Experimental Procedure
Results and Discussion
Conclusions and Future Work
Preliminaries Binary rating: Like/Dislike –
Explicit
Implicit - Based on user actions such as:
Buy
Click the item for additional details
Provide a recommendation of top n items
User selects from this list
Ignore the fact she can browse the remaining items.
We use a simple item-to-item NN CF
similarity measure such as Pearson correlation.
Item-to-Item NN CF with Binary Ratings rui* can be used to approximate the probability that user u would like item i. Some use Jaccard coefficient instead
Probabilistic Approach Employ rule of succession (Laplace correction)
find the conditional probability for positive response in the next presentation of item i to user u: where itemSim should be normalized such that:
Mathematical interlude: Rule of succession The proportion p of positive response is treated as a uniformly distributed random variable
Some claim that p is not random, but uncertain
We assign a probability distribution to p to express uncertainty, not to attribute randomness
Let Xi,j indicator variable
equals 1 when user i positively responded to an item j with probability pj of success (0 otherwise)
has a Bernoulli distribution.
Mathematical interlude: Rule of succession – cont. Suppose these Xs are conditionally independent given pj thus the likelihood is:
The conditional probability distribution of pj given the data Xi,j, i = 1, ..., n, is the multiplication of the "prior" (i.e., marginal) probability measure assigned to pj by the likelihood function (Bayes' theorem)
The posterior probability density function is
This is a beta distribution with expected value
Rule of succession implies
the conditional probability for positive response in the next presentation of item j given pj, is just pj. Mathematical interlude: Rule of succession – cont.
The Benefit and Risk of a Top 1 Recommendation A simple scenario:
Recommend the best (top 1) item from only two possible items P(u,i) r*ui Item 0.25 0.2 2 10 1 0.182 0.15 3 20 2 The risk:
The presented item (item1) is not selected by the user, but
if item 2 was presented to the user it would have been chosen
Risk Reduction Risk reduces as more ratings become available P(u,i) r*ui Item 0.227 0.2 4 20 1 0.166 0.15 6 40 2
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