In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.

1. Active Learning in Collaborative
Filtering RSs: a Survey
Mehdi Elahi
Francesco Ricci
Neil Rubens
August 2014
Munich, Germany
1
Corresponding journal article:
Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in
collaborative filtering recommender systems." Computer Science Review (2016).

2. Outline
¤ Introduction
¤ Cold Start Problem
¤ Active Learning in RS
¤ Conclusion and Future Works
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3. Introduction
¤ Recommender Systems are tools that support users
decision making by suggesting products that are
interesting to them.
¤ Collaborative Filtering: A technique used to predict
unknown ratings exploiting ratings given by users to
items.
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4. Cold Start Problem
¤ New User Problem: when
a new user has no rating
it is impossible to predict
her ratings.
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5
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¤ New item problem: when
a new item is added to
the catalogue and none
has rated this item it will
never be recommended.

5. Active Learning for Collaborative
Filtering
¤ Active Learning:
¤ Requests and try to collect more ratings from the
users before offering recommendations.
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6. Which Items should be chosen?
¤ Not all the ratings are equally useful, i.e.,
equally bring information to the system.
¤ To minimize the user rating effort only some
of them should be requested and acquired.
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7. An Illustrative Example
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Comedy movies
Si-Fi movies
Obscure movie
Zombie movies
Which Movie should be proposed to user to rate?

8. Definition of AL Strategy
¤ An active learning strategy for a Collaborative
Filtering is a set of rules to choose the best items
for the users to rate.
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9. How an AL Strategy works
Item Score
1 151
2 44
3 7
4 1
5 42
6 34
7 9
8 55
9 20
… …
N 12
System computes the
scores for all the
items that can be
scored (according to
a strategy)
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10. How an AL Strategy works
Top 10
items
Score
1 151
8 55
43 54
11 50
2 44
5 42
6 34
22 33
75 29
13 25
The system selects
the top 10 items
and presents them
to the simulated
user
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11. How an AL Strategy works
The items that are
rated are added to
the train set
Rated
items
1
2
5
75
13
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12. Classifying AL Strategies
A. Personalization: addresses the what extent the
personalization is performed when selecting the list of
candidate items for the users to rate
¤ Two Classes of Strategies:
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Non-personalized: are
those that ask all the
users to rate the same list
of items
Personalized: ask different
users to rate different
items – the best for each
user.

13. Classifying AL Strategies
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Personalization Dimension
Corresponding journal article:
Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey
of active learning in collaborative filtering recommender
systems." Computer Science Review (2016).

14. Classifying AL Strategies
B. Hybridization: whether the strategy takes into
account a single heuristic (criterion) for selecting the
items or combines several heuristics
¤ Two Classes of Strategies:
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Single-heuristic: are
those that implement a
unique item selection
rule.
Combined-heuristic strategies
hybridize single-heuristic
strategies by aggregating
and combining a number of
strategies.

15. Classifying AL Strategies
15
Hybridization Dimension
Corresponding journal article:
Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey
of active learning in collaborative filtering recommender
systems." Computer Science Review (2016).

16. Non Personalized AL: Classes and
Sub-Classes
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17. Non Personalized AL: Strategies
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18. Example of Non-Personalized AL
¤ Single Heuristics:
¤ Popularity: scores an item according to the frequency of
its ratings and then chooses the highest scored items
(Carenini, 2003)
¤ Entropy: scores each item with the entropy of its ratings
and then chooses the highest scored items (Rashid, 2002
and 2008)
¤ Combined Heuristics:
¤ log(Popularity)*Entropy: combines the popularity and
entropy scores and then chooses the highest scored
items (Rashid, 2002 and 2008)
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19. Personalized AL: Classes and Sub-
Classes
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20. Personalized AL: Strategies
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21. Example of Personalized AL
¤ Single Heuristics:
¤ Decision Tree Based: uses a decision tree whose nodes,
represents groups of users. Each node divides the users into
three groups based on their ratings: Lovers, Haters, and
Unknowns. Starting from the root node, a new user is proposed
to rate a sequence of items, until she reaches one of the leaf
nodes (Golbandi, 2011)
¤ Binary Prediction: scores an item according to the prediction
of its ratings (using transformed matrix of user-item) and then
chooses the highest scored items (Elahi, 2011)
¤ Combined Heuristics:
¤ Combined with Voting: scores an item according to the
votes given by a committee of different strategies and
then chooses the highest scored items (Elahi, 2011)
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22. Pros and Cons
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+ simple, fast, no training, serves users
with no rating, good for early stage
- less accurate, same items for all users
+ fast, benefits of multiple strategies
- flaws of multiple strategies, difficulty of
combining properly
+ accurate, different items for different
users, higher prob. of collecting ratings,
good for late stage
- complex, slow, needs training, cannot
serve users with no rating
+ accurate, great adaptivity to
condition of the system
- more complex, slowest

23. Conclusion
¤ We provided a comprehensive review of the state-of-
the-art on active learning in collaborative filtering
recommender systems
¤ We have classified a wide range of active learning
techniques, called Strategies, along the two
dimensions:
¤ how personalized these techniques are
¤ how many different item selection criteria (heuristics)
are considered by these strategies in their rating
elicitation process.
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24. Future Works
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¤ To survey works that have been done in AL
for other types of recommender systems,
such as content-based and context-aware.
¤ To analyze active learning techniques based
on their applicability to specific application
domains.

25. Thank you!
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August 2014
Munich, Germany
25
Corresponding journal article:
Elahi, Mehdi, Francesco Ricci, and Neil Rubens. "A survey of active learning in
collaborative filtering recommender systems." Computer Science Review (2016).