PrefWork is a framework for testing of meth- - it can be used for any given task, but it has to be ods of induction of user preferences. PrefWork is thor- customised, the developer has to choose from a very oughly described in this paper. A reader willing to use Pref- wide range of possibilities. For our case, Weka is too Work finds here all necessary information - sample code, strong. configuration files and results of the testing are presented in the paper. Related approaches for data mining testing are compared to our approach. There is no software available specially for testing of methods for preference learning to our best knowledge.
The most popular tool related to PrefWork is the open source projec t Weka [1]. Weka is in development for many years and has achieved to become the most widely used tool for data mining. It offers many classificators, regression methods, clustering, data preprocessing, etc. However this variability is also its weakness ? The work on this paper was supported by Czech projects MSM 0021620838, 1ET 100300517 and GACR 201/09/H057.
We also need some implementations of algorithms of the user preference learning that are publicly available for being able to compare various methods among themselves. This is a strength of PrefWork - any existing method, which works with ratings, can be integrated into PrefWork using a special adaptor Aggregation function may have different forms; one for each tool (see Section 4.3). There is a little bit of the most common is a weighted average, as in the old implementation of collaborative filtering Cofi [10] following formula: and a brand new one (released 7.4.2009) Mahout [11], developed by Apache Lucene project. Cofi uses Taste @(o) =(2 ∗ fP rice(o) + 1 ∗ fDisplay(o) + 3 ∗ fHDD(o)+ framework [12], which became a part of Mahout. The 1 ∗ fRAM (o))/7 , expectations are that Taste in Mahout would perform better than Cofi, so we will try to migrate our where fA is the fuzzy set for the normalisation of atPrefWork adaptor for Cofi to Mahout. Finally there is tribute A.
IGAP [13] - a tool for learning of fuzzy logic programs Another totally different approach was proposed in form of rules, which correspond to user preferences. in [15]. It uses the training dataset as partitioning of Unfortunately, IGAP is not yet available publicly for normalised space X0. For example, if we have an object download. with normalised values [0.4, 0.2, 0.5] with rating 3, any
We did not find any other mining algorithm spe- object with better attribute values (e.g. [0.5, 0.4, 0.7]) cialised on user preferences available for free down- is supposed to have the rating at least 3. In this way, load, but we often use already mentioned Weka. It we can find the highest lower bound on any object with is a powerful tool that can be more or less easily in- unknown rating. In [15] was also proposed a method tegrated into our framework and provide a reasonable for interpolation of ratings between the objects with comparison of a non-specialised data mining algorithm known ratings and even using the ideal (non-existent) to other methods that are specialised for preference virtual object with normalised values [1, ..., 1] with ratlearning. ing 6. 3
For making this article self-contained, we describe in brief our user model, as in [14]. In this section, we describe our user model. This model is based on a scoring function that assigns the score to every object. User rating of an object is a fuzzy subset of X(set of all objects), i.e. a function R(o) : X → [0, 1], where 0 means the least preferred and 1 means the most preferred object. Our scoring function is divided into two steps.
Local preferences In the first step, which we call local preferences, all attribute values of object o are normalised using fuzzy sets fi : DAi → [0, 1]. These fuzzy sets are also called objectives or preferences over attributes. With this transformation, the original space N of objects’ attributes X = Y DAi is transformed into 4
Our tool PrefWork was initially developed as a master thesis of Tom´aˇs Dvoˇr´ak [16], who has implemented it in Python. In this initial implementation, only Id3 decision trees and collaborative filtering was implemented. For better ease of use and also for the possibility of integrating other methods, PrefWork was later rewritten to Java by the author. Many more possibilities were added until the today state. In the following sections, components of PrefWork are described.
Most of the components can be configured by XML configurations. Samples of these configurations and Java interfaces will be provided for each component. We omit methods for configuration from Java interfaces such as configTest(configuration,section) which is configured using a configuration from a section in an XML file. Also data types of function arguments are omitted for brevity. 4.1
i=1 X0 = [0, 1]N . Moreover, we know that the object o ∈ X0 with transformed attribute values equal to [1, . . . , 1] is the most preferred object. It probably does not exist in the real world, though. On the other side, the object with values [0, . . . , 0] is the least preferred, which is more probable to be found in reality.
In this section a sample of workflow with PrefWork is described.
The structure of PrefWork is in Figure 1. There are four different configuration files - one for database access configuration (confDbs), one for datasources Global preferences In the second step, called global (confDatasources), one for methods (confMethods) preferences, the normalised attribute values are aggre- and finally one for PrefWork runs (confRuns). A run gated into the overall score of the object using an ag- consists of three components - a set of methods, a set gregation function @ : [0, 1]N → [0, 1]. Aggregation of datasets and a set of ways to test the method. Every function is also often called utility function. method is tested on every dataset using every way to
Test
Results Interpreter
CSV File
confDbs confDatasources confMethods confRuns Datasource
Inductive Method
Database/
CSV test. For each case, results of the testing are written tains a random number associated to each rating. Its into a csv file. purpose will described later.
A typical situation a researcher working with Every datasource has to implement the following PrefWork finds himself in is: “I have a new idea X. I am methods: really interested, how it performs on that dataset Y.”
The first thing is to create corresponding Java interface BasicDataSource{ class X that implements interface InductiveMethod boolean hasNextRecord(); (see 4.3) and add a section X to confMethods.xml. void setFixedUserId(value); Then copy an existing entry defining a run (e.g. IFSA, List<Object> getRecord(); see 4.5) and add method X to section methods. Run Attribute[] getAttributes(); ConfigurationParser and correct all errors in the new Integer getUserId(); class (and there will be some, for sure). After the run void setLimit(from, to, has finished correctly, process the csv file with results recordsFromRange); to see how X performed in comparison with other void restart(); methods. void restartUserId();
A similar case is when introducing a new dataset } into PrefWork - confDatasets.xml and confDBs.xml have to be edited if the data are in SQL database There are two main attributes of datasource - a list or in a csv file. Otherwise a new Java class (see 4.2) of all users and a list of ratings of the current user. able to handle the new type of data has to be created. getUserId returns the id of the current user. The For example, we still have not implemented the class most important function is getRecord, which returns for handling of arff files - these files have the defini- a vector containing the rating of the object and its tion of attributes in themselves, so the configuration attributes. Following calls of getRecords return all in confDatasets.xml would be much more simple (see objects rated by the current user. A typical sequence Section 4.2 for an example of a configuration of a data- is: source with its attributes). 4.2
Datasource is, as the name hints, the source of data for inductive methods. Currently, we are working only with ratings of objects. Data are vectors, where the first three attributes typically are: the user id, the object id and the rating of the object. The attributes of the object follow. There is a special column that con- } int userId = data.getUserId(); data.setFixedUserId(userId); data.restart(); while(data.hasNextRecord()){
List<Object> record =
data.getRecord(); // Work with the record ... Another important function is setLimit, which limits Let us also note the select for obtaining the user the data using given boundaries from and to. The ids (section usersSelect) and the name of the column random number associated to each vector returned that contains the random number used in setLimit by getRecord has to fit into this interval. If (randomColumn). recordsFromRange is false, then the random number should be outside of the given interval on the contrary. Other types of user preferences. PrefWork as it is This method is used when dividing the data into train- now supports only ratings of objects. There are many ing and testing sets. For example, let us divide the data more types of data containing user preferences - user to 80% training set and 20% testing set. First, we call clickstream, user profile, filtering of the result set etc. setLimit(0.0,0.8,true) and let the method train PrefWork does not work with any information on these data. Then, setLimit( 0.0,0.8,false) is about the user, either demographic like age, sex, place executed and vectors returned by the datasource are of birth, occupation etc. or his behaviour. These types used for the testing of the method. of information may bring a large improvement in the
Let us show a sample configuration of a datasource prediction accuracy, but they are typically not present that returns data about notebooks: - users do not want to share any personal information for the sole purpose of a better recommendation.
Another issue is the complexity of user information; a semantic processing would have to be used. <NotebooksIFSA> <attributes> <attribute><name>userid</name>
<type>numerical</type> </attribute> <attribute><name>notebookid</name>
<type>numerical</type> </attribute> <attribute><name>rating</name>
<type>numerical</type> </attribute> <attribute><name>price</name>
<type>numerical</type> </attribute> <attribute><name>producer</name>
<type>nominal</type> </attribute> <attribute><name>ram</name>
<type>numerical</type> </attribute> <attribute><name>hdd</name>
<type>numerical</type> </attribute> </attributes> <recordsTable>
note_ifsa </recordsTable> <randomColumn>
randomize </randomColumn> <userID>userid</userID> <usersSelect> select distinct userid from note_ifsa </usersSelect> </NotebooksIFSA> First, a set of attributes is defined. Every attribute has a name and a type - numerical, nominal or list.
An example of list attribute is actors in a film. This attribute can be found in the IMDb dataset [17].
InductiveMethod is the most important interface - it is what we want to evaluate. Inductive method has two main methods: interface InductiveMethod { int buildModel(trainingDataset,
userId); Double classifyRecord(record,
targetAttribute); } buildModel uses the training dataset and the userId for the construction of a user preference model. After having it constructed, the method is tested - it is being given records via method classifyRecord and is supposed to evaluate them.
Various inductive methods were implemented.
Among the most interesting are our method Statistical ([18, 15] ) and Instances ([15]), WekaBridge that allows to use any method from Weka (such as Support vector machine) and ILPBridge that transforms data to a prolog program and then uses Progol [19] to create the user model. CofiBridge allows to use Cofi as a PrefWork InductiveMethod.
A sample configuration of method Statistical is: <Statistical> <class>Statistical</class> <rater> <class>WeightAverage</class> <weights>VARIANCE</weights> </rater> <representant>
<class>AvgRepresentant</class> </representant> <numericalNormalizer> <IFSA>
Linear <methods> </numericalNormalizer> <method> <nominalNormalizer> <name>Statistical</name>
RepresentantNormalizer <numericalNormalizer> </nominalNormalizer> Standard2CPNormalizer <listNormalizer> </numericalNormalizer>
ListNormalizer </method> </listNormalizer> <method><name>Statistical</name> </Statistical> </method> <method><name>Mean</name></method> Every method requires a different configuration, only <method><name>SVM</name></method> the name of the class is obligatory. Note that the </methods> methods based on our two-step user model (Statis- <dbs> tical and Instances for now) can be easily configured <db> to test different heuristics for the processing of differ- <name>MySQL</name> ent types of attributes. Configuration contains three <datasources>NotebooksIFSA sections: numericalNormalizer, nominalNormalizer </datasources> and listNormalizer for the specification of the </db> method for the particular type of attribute. Also see </dbs> Section 4.5 for an example of this configuration. <tests> <test> 4.4 Ways of the testing of the method <class>TestTrain</class> <ratio>0.05</ratio> Several possible ways for the testing of methods can <path>resultsIFSA</path> be defined, the division to training and testing sets <testInterpreter> is the most typically used. The method is trained on <class>DataMiningStatistics the training set (using buildModel) and then tested on </class> the testing set (using classifyRecord). Another typical </testInterpreter> method is k-fold cross validation that divides data into </test> k sets. In each of k runs, one set is used as the testing <test> set and the rest as the training set. <class>TestTrain</class> interface Test { <ratio>0.1</ratio> void test(method, trainDataSource, <path>resultsIFSA</path>
testDataource); <testInterpreter> } <class>DataMiningStatistics </class>
When the method is tested, the results in the form </testInterpreter> userid, objectid, predictedRating, realUserRating </test> have to be processed. The interpretation is done by </tests> a TestResultsInterpreter. The most common is </IFSA> DataMiningStatistics, which computes such measures as correlation, RMSE, weighted RMSE, MAE, Kendall rank tau coefficient, etc. Others are still waiting to be implemented - ROC curves or precision-recall statistics.
First, we have specified which methods are to be tested - in our case it is two variants of Statistical, then Mean and SVM. Note that some attributes of Statistical, which was defined in confMethods, can be abstract class TestInterpreter { “overridden” here. The basic configuration of Statistiabstract void writeTestResults( cal is in Section 4.3. Then the datasource for testing of testResults); the methods is specified – we are using MySql database } with datasource NotebooksIFSA. Several datasources or databases can be specified here. Finally, the ways 4.5 Configuration parser of the testing and interpretation are given in section tests. TestTrain requires ratio of the training and the The main class is called ConfigurationParser. The de- testing sets, the path where the results are to be writfinition of one test follows: ten, and the interpretation of the test results. date;Ratio;dataset;method;userId;mae;rmse;weightedRmse;monotonicity;tau;weightedTau;correlation;buildTime; testTime;countTrain;countTest;countUnableToPredict
28.4.2009 12:18;0,05;NotebooksIFSA;Statistical,StandardNorm2CP;1;0,855;0,081;1,323;1,442;0,443;0,358;0,535;94;47;10;188;0; 28.4.2009 12:18;0,05;NotebooksIFSA;Statistical,StandardNorm2CP;1;0,868;0,078;1,216;1,456;0,323;0,138;0,501;32;0;13;185;0; 28.4.2009 12:18;0,05;NotebooksIFSA;Statistical,StandardNorm2CP;1;0,934;0,083;1,058;1,873;0,067;0,404;0,128;31;16;12;186;0; 28.4.2009 12:31;0,025;NotebooksIFSA;Statistical,Peak;1;0,946;0,081;1,161;1,750;0,124;0,016;0,074;15;16;4;194;0 28.4.2009 12:31;0,025;NotebooksIFSA;Statistical,Peak;1;0,844;0,076;1,218;1,591;0,224;0,215;0,433;0;16;6;192;0 28.4.2009 12:31;0,025;NotebooksIFSA;Statistical,Peak;1;1,426;0,123;1,407;1,886;0,024;0,208;-0,063;16;0;4;194;0
The definitions of runs are in confRuns.xml in section runs. The specification of the run to be executed is in section run of the same file. In Figure 2 is a sample of the resulting csv file. In our example, there are three runs with method Statistical with normaliser StandardNorm2CP and three runs with normaliser Peak. Runs were performed on different settings of the training and the testing sets, so the results are different even for the same method.
The results contain all necessary information required for generation of a graph or a table with the results. Csv format was chosen for its simplicity and wide acceptance, so any other possible software can handle it. We are currently using Microsoft Excel and its Pivot table that allows aggregation of results by different criteria. Among other possibilities is also the already mentioned R [3].
Example figures of the output of PrefWork are in Figures 3 and 4. The lines represent different methods, X axis represents the size of the training set and the Y axis the value of the error function. In Figure 3 the error function is Kendall rank tau coefficient (the higher it is the better) and in Figure 4 is RMSE weighted by the original rating (the lower the better).
The error function can be chosen, as is described in Section 4.4.
It is impossible to compare PrefWork to another framework generally. A simple comparison to other such systems is in Section 2. This can be done only qualitatively; there is no attribute of frameworks that can be quantified. The user itself has to choose among them the one that suits his needs the most. 4.7
PrefWork is dependent on some external libraries. Two of them are sources for inductive methods - Weka [1] and Cofi [10]. Cofi also requires taste.jar. 0,7 0,6 0,5 t n e iic0,4 f f e co0,3 u a T0,2 0,1
0 1,55 1,35 E S 1RM,15 d e t h g i e 0W,95 0,75 0,55
Average of Tau coefficient weka,SVM Mean Statistical, Linear regression Statistical,2CP-regression weka,MultilayerPerceptron 2 5 10 15 20 Training set size 40
75
Fig. 3. Tau coefficient.
Average of Weighted RMSE weka,SVM Mean Statistical, Linear regression Statistical,2CP-regression weka,MultilayerPerceptron 2 5 10
15 Training set size 20 40
75
Fig. 4. Weighted RMSE.
PrefWork requires following jars to function cor- Eliassi-Rad, T., eds.: KDD’06: Proceedings of the 12th rectly: ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM Weka weka.jar (August 2006), 935–940.
Cofi cofi.jar 3. R-project. http://www.r-project.org/. Cofi taste.jar 4. SAS enterprise miner. http://www.sas.com/. Logging log4j.jar 5. SPSS Clementine. http://www.spss.com/software/ CSV parsing opencsv-1.8.jar modeling/modeler/.
Configuration commons-configuration-1.5.jar 6. Sˇ. Pero, T. Horv´ath: Winston: A data mining assisConfiguration commons-lang-2.4.jar tant. In: To appear in proceedings of RDM 2009, 2009. MySql mysql-connector-java-5.1.5- 7. P. Viappiani, B. Faltings: Implementing example-based bin.jar tools for preference-based search. In: ICWE’06: ProOracle ojdbc1410.2.0.3.jar ceedings of the 6th international conference on Web engineering, New York, NY, USA, ACM, 2006, 89–90.
Tab. 1. Libraries required by PrefWork. 8. P. Viappiani, P. Pu, B. Faltings: Preference-based search with adaptive recommendations. AI Commun. 21, 2-3, 2008, 155–175. 5 Conclusion 9. S. Holland, M. Ester, W. Kiessling: Preference mining: A novel approach on mining user preferences for personalized applications. In: Knowledge Discovery in PrefWork has been presented in this paper with a thor- Databases: PKDD 2003, Springer Berlin / Heidelberg, ough explanation and description of every component. 2003, 204–216.
Interested reader should be now able to install Pref- 10. Cofi: A Java-Based Collaborative Filtering Library. Work, run it, and implement a new inductive method http://www.nongnu.org/cofi/. or a new datasource. 11. Apache Mahout project. http://lucene.apache.
The software can be downloaded at http://www. org/mahout/. ksi.mff.cuni.cz/∼eckhardt/PrefWork.zip 12. Taste project. http://taste.sourceforge.net/old. as an Eclipse project containing all java sources and all html. required libraries or can be downloaded as SVN check- 13. T. Horv´ath, P. Vojt´aˇs: Induction of fuzzy and annoout at [20]. The SVN archive contains Java sources and tated logic programs. In Muggleton, S., TamaddoniNezhad, A., Otero, R., eds.: ILP06 - Revised Selected sample configuration files. papers on Inductive Logic Programming. Number 4455 in Lecture Notes In Computer Science, Springer Ver5.1 Future work lag, 2007, 260–274. 14. A. Eckhardt: Various aspects of user preference We plan to introduce time dimension to PrefWork. learning and recommender systems. In Richta, K., Netflix [21] datasets uses a timestamp for each rat- Pokorny´, J., Sn´aˇsel, V., eds.: DATESO 2009. CEUR ing. This will enable to study the evolution of the Workshop Proceedings, Cˇ esk´a technika - nakladatelpreferences in time, which is a challenging problem. stv´ı Cˇ VUT, 2009, 56–67.
However, the integration of the time dimension into 15. A. Eckhardt, P. Vojt´aˇs: Considering data-mining techniques in user preference learning. In: 2008 InternaPrefWork can be done in several ways and the right tional Workshop on Web Information Retrieval Supone is yet to be chosen. port Systems, 2008, 33–36.
Allowing other sources of data apart from the rat- 16. T. Dvoˇr´ak: Induction of user preferences in semanings is a major issue. The clickthrough data can be tic web, in Czech. Master Thesis, Charles University, collected without any effort of the user and can be sub- Czech Republic, 2008. stantially larger than the number of ratings. But its in- 17. The Internet Movie Database. http://www.imdb. tegration into com/.
PrefWork would require a large reorganisation of ex- 18. A. Eckhardt: Inductive models of user preferences for isting methods. semantic web. In Pokorny´, J., Sn´aˇsel, V., Richta, K., eds.: DATESO 2007. Volume 235 of CEUR Workshop Proceedings., Matfyz Press, Praha, 2007, 108–119.
References 19. S. Muggleton: Learning from positive data. 1997, 358– 376 20. PrefWork - a framework for testing methods for user preference learning. http://code.google.com/p/ prefwork/. 21. Netflix dataset, http://www.netflixprize.com.