Proceedings of the 2004 IEEE
Conference on Cybernetics and Intelligent Systems
Singapore, 1-3 December, 2004��

A Movie Recommender System Based on
Inductive Learning
PENG LISEIJI YAMADA

CISS, IGSSE, Tokyo Institute of Technology, Japan4259 Nagatuta, Midori-ku, Yokohama, Japanliorlee@nii.ac.jp

National Institute of Informatics, Japan
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japanseiji@nii.ac.jp

Abstract—Recommender Systems apply intelligent accesstechnologies to large information systems. These systems,
especially collaborative filtering based ones, are achieving
widespread success on the Web. In recent years, the amount ofavailable information and the number of visitors to Web sites are
increasing enormously. New recommender system technologies
are needed that can quickly produce high quality
recommendations, even for very large-scale information
resources. In this paper we apply inductive learning algorithm tothe recommendation process. Instead of computing user-user or
item-item similarities, we construct a decision tree to represent
user preference. Recommendations are performed by decision
tree classification. To inspect the effectiveness of this technology,
we set up a movie recommender system based on inductive
learning and make online experiments for evaluation. Our results
suggest that inductive-learning-based technology is promising forthe solution of the very large-scale problems and high-quality
recommendations can be expected.

poses some potential challenges such as sparsity andscalability problems[5]. In addition, collaborative filtering has
a typical problem that the reason and the adequacy ofrecommendation is not transparent.

Keywords—recommender system; inductive learningI.
INTRODUCTION
With the dramatic development of computer and internet
technologies, everyone is capable of accessing the tremendous
amount of information easily. Moreover, the amount of
information in the world is increasing far more quickly than
our ability to process it. Especially in recent years, many
contents providers begin to provide news, music and movie
resources. It is difficult to obtain relevant contents through theflood of these resources.
One of the approach to solve this problem is thedevelopment of recommender systems[1]. Recommendersystems
canautomatically
apply
personalizedrecommendations for information, products or services during
a live interaction.
The elemental technology of recommender systems iscollaborative filtering. Collaborative filtering is a technique by
which the interest of a user for an object is predicted from theknowledge of the interest of other users for this object. Manybusiness systems such as LikeMinds (Macromedia,Inc),
GroupLen(NetPerceptions,Inc), AuraSearch(AuraLine&FEG)
are based on collaborative filtering techniques. Collaborative
filtering has achieved great success in both academic andbusiness fields[2,3,4]. However, the tremendous growth in theamount of available information and the number of users

0-7803-8643-4/04/$20.00 © 2004 IEEE

In this research, we propose a new recommendation
technology based on inductive learning in the attempt ofsolving the sparsity, scalability and transparency problems.
Our basic idea can be described in three steps: First,
correspond an item’s evaluated value and its attributes to theclass and attributes of a decision tree. Secondly, perform
inductive learning to construct decision trees that represent
user’s preferences. Finally, obtain predicted value of a newitem through decision tree classification.
Inductive-learning-based recommendation technology
solves the sparsity problem by sharing attributes of each
item(we call them contents preferences). The computation cost
during inductive learning process is low enough to constructdecision tree instantly. Moreover, the computation cost duringrecommendation process has a linear relationship to the total
amount of available items. Therefore it will not become
enormous with the growth of scalability.
In order to inspect the effectiveness of this technology, we
set up a movie recommender system based on inductive
learning and make online experiments for evaluation. There
are two reasons why we choose movie data as our target. One
reason is the compilation of movie database is sophisticatedand many solid movie databases like IMDb1 (Internet Movie
Database) are available on the Web. The other reason is that
we can obtain the contents preferences of each item from an
online database called PAOON 2 directly, which play animportant role in the recommendation process.
We begin with a brief overview of traditional
recommendation
technologies.
Inductive-leaning-based
recommendation technology is then introduced, followed by adetailed description of our movie recommender system.
Evaluation experiment is then discussed. Finally, we present
some tentative conclusions and recommendations for further
study.

3181

http://www.imdb.com/

2

http://www.paoon.com/

II.

OVERVIEW OF TRADITIONAL RECOMMENDATION TECHNOLOGIES

A. CONTENTS-BASED FILTERING
Information filtering techniques fall in two independent
categories: content-based filtering and collaborative filtering.
Content-based filtering is based on content analysis of theconsidered objects, e.g. term frequency for documents, and its
relation to the user's preferences. For content-based filtering itis therefore necessary that the results of content analysis anduser preferences can reliably and automatically be determined.
While recent research shows good results for the contentbased filtering of documents, filtering of other media, as audio
and video, is hard due to the limitations of content analysis
technology available. Collaborative filtering, on the other
hand, does not show this limitation.
B. COLLABORATIVE FILETERING
Collaborative filtering is based on the premise that people
looking for information should be able to make use of what
others have already found and evaluated. In collaborative
filtering, objects are selected for a particular user when they
are also relevant to similar users and, in general, the content of
the objects is ignored. Therefore, collaborative filtering is
especially interesting for items for which content analysis isdifficult or impossible.
Collaborative filtering based recommender systems areachieving widespread success on the Web. However, thetremendous growth in the amount of available information andthe number of users poses some potential challenges. Theseare:●SPARSITY
The sparsity problem occurs under the situation that theamount of evaluated items of each user is by far less than the
total amount of available items. Many commercial
recommender systems are used to evaluate large item
sets(e.g.,Amazon.com recommends books an CDnow.com
recommends music albums). In these systems, even active
users may have purchased under 1% of the items[5]. It isdifficult to find neighbors in this situation. As a result, theaccuracy of recommendations may be poor.
●SCALABILITY
Nearest neighbor algorithms require computation that grows
with both the number of users and the number of items. Withmillions of users and items, a typical web-based recommendersystem running existing algorithms will suffer serious
scalability problems[5].
●TRANSPARENCY
The relationship between the recommended information anduser preferences is not clear. It is difficult for a user tounderstand how the items are recommended.
III. INDUCTIVE-LEARNING BASEDRECOMMENDATION TECHNOLOGY
A. INDUCTIVE-LEARNING AND DECISION TREE
Inductive Learning is a typical learning task in machinelearning. Given a data set, inductive learning aims to discover

Figure 1. Decision tree constructed by C4.5.

patterns in the data and form concepts that describe the data.
Research in inductive learning has sustained for decades.
A decision tree is a tree in which each “branchnode”(attribute) represents a choice between a number ofalternatives, and each “leaf node”(class) represents aclassification or decision. Two well-known algorithms forconstructing decision trees are C4.5 [Quinlan 1993] [6]and
CART (Classification and Regression Tree) [Breiman et al.
1984].
C4.5 is a typical machine learning program for the efficientsynthesis of decision trees. Figure 1 shows an example ofdecision tree constructed by C4.5.
B. RECOMMENDATION WITH C4.5
The detailed recommendation process based on inductive
learning is listed below:(1) Input of training data
The user evaluates several items through GUI.
(2) Construction of decision tree
Correspond an item’s evaluated value and its contents
preference to the class and attributes of a decision tree. The
decision tree constructed is used as a user profile.
(3) Classification of unvalued items
Use the decision tree constructed at step (2) to classify theitems that are not evaluated by the user. The decision treeclasses are corresponded to predicted values. Each unvalued
item is classified according to its contents preferences and getsa particular predicted value.
(4) Building of candidates list

319

TABLE I. CREDIT PREFERENCE

Credit Preference
Genre
Director
Starring
A Beautiful Mind
Drama; Romance
Ron Howard
Russell Crowe;Jennifer Connelly
Top GunAction; Romance
Tony Scott
Tom Cruise; Kelly McGillis
the Godfather
Drama; Suspense/Crime Francis Ford Coppola
Marlon Brando; Al Pacino
Saving Private Ryan
War; Drama
Steven Spielberg
Tom Hanks; Matt Damon

C. ADVANTAGES OF INDUCTIVE-LEARNING-BASEDRECOMMENDATION
Inductive-learning-based recommendation technology isavailable for the solution of the following problems.
●SPARSITY
Instead of computing user-user or item-item similarities,
inductive-learning-based recommendation technology looks
into the contents preference of each item. Contents
preferences are set up in advance by users or administrators
and are shared among users. Data will not get sparse because
any item which has a contents preference can obtain its
predicted value through decision trees.
●SCALABILITY

Figure 2. Contents preference.

In C4.5 learning algorithms, the computation cost depends
on the number of training data. If m stands for the number of2

training data, the computation cost is O(m ) . In practice, thenumber of items a user evaluates is no more than one hundred,
so a decision tree can be generated immediately. In the case ofm>1000, we use alternative sampling method[7] to reduce thecomputation cost.
The computation cost during recommendation processdepends on the total amount of available items. If n stands forthe total amount of available items, the computation cost isO(n) because they have a linear relationship. Therefore thecomputation cost will not become enormous with the growthof the number of users and items.
●TRANSPARENCY

Figure 3. System construction.

High readability is one of the advantages of decision trees.
The relationship between the recommended information anduser preferences is clear. Furthermore, the structure of thedecision tree itself represents user preferences, so detailed
information can be offered.
IV.

Sort the unvalued items by its predicted values to build
candidates list.
(5)Result presentation
Sort candidates list and present it to users through GUI.
(6)Training data addition
If a recommended item is evaluated by the user, add thisitem to the training data. It takes part in the construction of thedecision tree in the next recommendation.

A MOVE RECOMMENDER SYSTEM BASED ON
INDUCTIVE LEARNING
We setup a movie recommender system to inspect theeffectiveness of inductive-learning-based recommendation
technology. In this section, We give detailed description about
this system.
A. CONTENTS PREFERENCE AND CREDIT
PREFERENCE
We use two types of movie data in this system. They arecontents preference and credit preference.
Contents
preference has 7 attributes, as shown in Figure 2, story,
comedy, action, emotion, warmth, horror and performance.

320Figure 4. User interface.

Each attribute has an integral value from 1 to 5. Higher
value stands for better quality. Contents preference plays animportant role in decision tree construction and classification.
Credit preference is the credit information of a movie, as
shown in TAB I, such as genre, director and starring.
Recommendation with same training data always provides astatic result. Specifying credit preference enables dynamic
recommendations.
B. SYSTEM CONSTRUCTION AND RECOMMENDATION
FLOW
System construction is shown in Figure 3. Through GUI
interface, a user evaluates several movies and provides themto C4.5 as training data. C4.5 apply these training data x toconstruct the decision tree ht : x− > y(1 ≤ t ≤ 5) . Use thedecision tree to classify unvalued items and build candidates
list. Evaluation engine analyzes user specified credit
preference, sorts candidates list and presents recommendation
results to the user

C. USER INTERFACE
Figure 4 is the user interface of our movie recommendersystem. A user will come to this page after login. Each page
presents 10 items. “Predictions for you” shows the system
predicted value. 5 stars is the highest recommendation. “YourRatings” stands for the evaluated value by the user. If one of
the movies on the list has been previously experienced, a user
can perform evaluation by selecting a value and “Submit” it to
the server. “Movie Information” displays credit information.
Users can click “IMDd” for more detailed information. This
design is for better usability[8].
Evaluations by the user are saved after logout. The
evaluated items take part in the construction of the decision
tree in the next recommendation.
D. SORTING CANDIDATES LIST WITH CREDIT
PREFERENCE
Credit preference is information such as genre, director andstarring. Evaluation engine first checks whether genre is
specified or not. If specified, delete items in candidates list321

that are not in this genre. Next, collect all items that are
directed or cast by the specified directors or stars. Take theaverage of each attribute Attribute k of the contents
preference E k =∑ Attributekii. Use the attribute that getsnthe highest score Attributemax to sort items with the same
predicted values. Then carry out sorting with the second
highest attribute Attributemax −1 . For example, if director is
specified to “Steven Spielberg”, items that have the same
predicted value as 5 are sorted by the attribute “story”. Thenitems that have the same predicted value and the same valueof the attribute “story” are sorted by the attribute“performance”.
V.

Figure 5. Transition of MAE(1).

EVALUATION EXPERIMENT

A. PARTICIPANTS
A total of 20 university students participated in our
experiment. Age range: 19 to 23 years. Gender ratio: 18 males
and 2 females. Technical background: 14 were students intechnology-related fields, the other 6 were studying in nontechnical fields.
B. PROCEDURE(1) Online registration(2) Evaluate items in order to get recommendations(3) Review the recommendation list. Items that have beenpreviously experienced should be evaluated(4) Specify credit preference. Repeat (3)(5) Experiment finishes when the evaluated items reach acertain number
C. EVALUATION METRICS
Statistical accuracy metrics evaluate the accuracy of asystem by comparing the numerical recommendation scoresagainst the actual user ratings. MAE(Mean Absolute Error)
between ratings and system predictions is a widely usedmetric. MAE is a measure of the deviation ofrecommendations from their true user-specified values. For
each ratings-prediction pair < pi , qi > this metric treats theabsolute error between them equally. The MAE is computedby first summing these absolute errors of the N corresponding
ratings-prediction pairs and then computing the average[5].
Formally,N∑| p − q |MAE = i =1

i

i

N

The lower the MAE, the more accurately therecommendation engine predicts user ratings. We use MAE as
our choice of evaluation metric to report prediction
experiments because it is most commonly used and easiest tointerpret directly.

Figure 6. Transition of MAE(2).

D. PILOT STUDY
Before evaluation experiment, we should fix 2 parameters,
which affect the experimental result materially. One is thenumber of training data. It’s necessary to investigate theminimum number of the training data required in the decision
tree construction process. The other is the number of test data
for evaluation. During the evaluation experiment, we should
figure out how many items a user should evaluate in order tomake efficient evaluation.
Figure 5 shows the transition of the average and thestandard deviation of MAE when the number of training data
is 5, 10, 15, 20, 25 and 30. 25 times of cross-validation were
executed for each number of training data. We can see that the
average of MAE drops with the increase of the number oftraining data. Furthermore, the average of MAE is virtuallyconstant after 10 in spite of the rapid fall between 5 and 10.
The average of MAE is 0.455 when the number of training
data is 10, which decreases 0.167 compared to 5, but only
exceeds 30 by 0.077. Therefore, the efficient decrease of MAE
cannot be expected even if we boost the number of training
data. The number of training data was fixed to 10 in the later
experiments.
Figure 6 shows the transition of the average and thestandard deviation of MAE when the number of training data
is 10 and the number of test data for evaluation is 1, 3, 5,
10,15, 20, 25 and 30. 25 times of cross-validation were322

recommender systems. We set up a movie recommendersystem to inspect the effectiveness. Our experimental resultshows that this new recommendation technology is availableof solving the Sparsity and Scalability problems, while at thesame time providing high quality recommendations.

REFERENCES[1][2]

Figure 7. Experimental result.

executed for each number of test data. We can see that the
average of MAE is virtually constant with the increase of test
data. When the number of test data is 1 and 3, the standard
deviation exceeds the average of MAE. Considering thereliability, we fixed the number of test data for evaluation to 5
in the later experiments.
E. EXPERIMENTAL RESULT
Figure 7 shows the result of our experiment. The number ofthe items a participant should evaluate is 15 in total, of which
10 are training data and 5 are test data for evaluation. The
average of MAE is 0.63, and the standard deviation is 0.37.

[3]

[4]

[5]

[6]
[7]

[8]

F. DISCUSSION
In this experiment, we used only 10 training data tomoderate MAE under 0.7. MAE was 0.7 to 0.8 in theevaluation experiment by MovieLens [Sarwar,2001][5]. We
cannot perform quantitative comparison because the data set
and the evaluation metrics are very different. However, anunder 1.0 MAE means that most of the time, system
predictions agrees user ratings or have the difference of 1 rank.
It is almost certain that our system provided high quality
recommendations.
We did this experiment online. The execution speed of theserver program is 2 to 3 seconds. This is an acceptable speed
for online users.
One limitation of our experiment design is that we choose
20 university students as our participants. If the age range
stretches and the number of participants grows, we do nothave the assurance that we will obtain the same result. Moreexperimental data are needed to improve the reliability.
The experimental result has gaps between some users. We
looked into the cases that received high MAE and found thatthe recommendation accuracy drops if the evaluated value of
training data has large bias. During the decision treeclassification, 14% of the test data failed to be classified.
Increasing the number of attributes of contents preference willlead to a more accurate classification.
VI. CONCLUSION
In this research, we pose a new recommendation technology
based on Inductive Learning in the attempt of solving thepotential challenges of collaborative filtering based

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