Integrating a Recommender System Into DSpace

8/17/2019 Integrating a Recommender System Into DSpace

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Integrating a Recommender System into Dspace

Jonathan Blood



Introduction

This paper will take a look at integrating a item based recommender system into DSpace with the

help of Apache Mahout. Apache Mahout is a suite of machine learning libraries designed to be

scalable and robust.[1]

Recommender Systems are software tools which provide suggestions for items that a user may be

interested in.[2] For a breakdown of the different kind of recommender systems take a look at the

Wikipedia page for recommender systems. For DSpace this will require implementing a

collaborative item based recommender system that will rely on users rating DSpace items in order

to generate accurate recommendations.

An example of a recommender system that is used on http://www.amazon.co.uk

In order to implement a recommender system in DSpace there are a number of steps that will need

to be completed:

1.

Create a table in the DSpace database in order to store the users recommendations.

2. Create a way to manipulate this table with basic operations: create, read, update

and delete.

3. Create the recommender system with the help of Mahout.

4. In the user interface of DSpace create a method to add or update a rating on a DSpace

item and provide a list of recommended items on the item display page.

Development

This chapter will focus on integrating the recommender system into the DSpace application. The

implementation will be reviewed in more detail under the four headings:

1. Storage



2. Database Helper Class for the Ratings Table

3. Creating the Recommender System

4. User Interface Changes

For the full implementation of the recommender system into DSpace check out this Github page.

Note: The following will denote the DSpace 5.2 source directory <dspace-src>.

Step 1: Storage

This step will look at creating the database table that will be used to store user ratings for items in

DSpace.

The table will consist of the following columns:

rating_id Primary key so it is possible to uniquely select records.

eperson_id The users unique identifier.

dspace_object_id This will store the unique identifier of the item.

rating A rating between 1-5 (higher the better).

In <dspace-src>/dspace-api/src/main/resources/org/dspace/storage/rdbms/sqlmigration/postgres/ create a file called

V5.2__recommender_system_schema.sql and insert the following:

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By creating this file with this particular naming convention this SQL script will be picked up by

DSpace on initial installation or upgrade to DSpace 5.2 and will automatically import this

database table and sequence into the DSpace database.

Step 2: Database Helper Class for the Ratings Table

In the previous step the database table to store the user ratings was created. This step will look at

creating a helper class which will perform some basic manipulation of this table. This class will

need the following methods:



addRating() Adds a rating record to the table.

hasRating() Checks if a record already exists for given eperson and item.

getRating() Get a users rating on an item.

updateRating() Update an existing ratings value.

deleteObjectRating() This will be called when an item is deleted. This method willdelete all rating records for a given item.

These methods will make use of the DatabaseManager class in DSpace to help manipulate this

table. In <dspace-src>/dspace-api/src/main/java/ create a package called com.jonathanblood.content . In

this package create a class called RatingsManager.java:

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Step 3: Creating the Recommender System

This step will look at creating the recommender system in DSpace with the help of the Mahout

libraries. Before looking at the recommender implementation the DSpace project will require the

external Mahout libraries.

The external Mahout libraries can be included into the DSpace project with the help of Maven. In

<dspace-src>/dspace-api/pom.xml add the following dependencies:

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When Dspace is built and compiled this will download the Mahout libraries that are needed. Now

that the external libraries are sorted it’s possible to implement the recommender class and break

down how this implementation works. The class will consist of a single method called

getRecommendations() that will take in a item id and a number of recommended items as a

parameter. The method will return a list of recommendations that were calculated.



In <dspace-src>/dspace-api/src/main/java/ create a package called com.jonathanblood.recommender . In

this package create a class called Recommender.java:

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It’s time to breakdown the logic of the getRecommendations() method:X$.$:/,0-' )$.$:/,0-' ] X$.$%$*'K$($2'0U2'.X$.$:/,0-'BN?

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In this code snippet a connection to the database is obtained using the DatabaseManager class in

DSpace. After this a data model for PostgeSQL is instanciated that will allow for the ability

to load data from the ratings table found in the DSpace database.

Note: In this paper the assumption here is that PostgreSQL is used for the database in DSpace.

Mahout also supports other ways of loading data into the data model such as from a MySQL

database or from a file.

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In the following code snippet the LogLikelihoodSimilarity is used in order to calculate the

recommended items for the recommender system.

The Tanimote coefficient is the ratio count of the number of items two users have in common

divided by the number of items both users have a preference for. The problem with this approach

is that in a large data set there is a good chance that two users will have overlapping similarities in

items. The LogLikelihoodSimilarity helps to alleviate this problem by also taking into account the

overlap of two users items that happens by chance.[3]

Mahout comes with a number of different similarity methods that can be used instead

of LogLikelihoodSimilarity such as:

•

PearsonCorrelationSimilarity

• TanimoteCoefficientSimilarity

• SpearmanCorrelationSimilarity

In the next line of code the recommender system takes in the datamodel (data) and the similarity

calculation ( LogLikelihoodSimilarity).

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The purpose of this code snippet is to produce recommendations by passing in the item id and the

number of recommendations that should be returned. This is calculated by using the data from the

model and the similarity calculation that was passed into the GenericItemBasedRecommender .

Step 4: User Interface Changes

In the previous step the recommender class was created which can generate a list of recommended

items in DSpace. The following needs to be done now:

• Add a way for users to add and update ratings for an item.

•

On the item display page list item recommendations that are returned by the

recommender system.

In DSpace there are two User Interfaces JSPUI and XMLUI. For this paper it will only

be implemented for the XMLUI interface. On the DSpace item display page will be an option for

the user to add/update a rating for an item and also list a number of recommended items. These

elements will be implemented below. In <dspace-src>/dspace-

xmlui/src/main/java/org/dspace/app/xmlui/aspect/artifactbrowser/ItemViewer.java modify theaddBody() method as

following:

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This snippet of code does two things:

1. Creates a form for setting or updating a rating for an item. The form is quite simple and

consists of a dropdown list with rating values from 1-5, hidden fields for passing the user

id and item id of an item and a submit button. The form is submitted to a add-rating

action that will handle the form processing for the rating.

2. Below the add ratings form a list of recommended items are shown.

Note: Ideally there would be some kind of skin to the dropdown list (i.e. display 5 stars) or instead

of showing just the title of the recommended items it would also show the items thumbnail if it

was available just so it is more visually appealing to the user.



Ratings form and recommendation list

Since the item display page is modified to include an item rating this will require a change to how

caching works on this page. To see how caching is currently implemented on the item display page this can be seen by taking a look at how the getKey() method in ItemViewer.java:



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Based on the cache key above it can be seen that a item’s cache will be invalidated when an items

metadata is changed (as showFullItem(objectModel) will change). Since a rating is something that

can change and is not static this will require extending the caching key to include an eperson id

and a rating. That is assuming the user has provided a rating to the item in question. This change

will result in the caching key becoming invalidated when a items rating is added or updated. This

is achieved by modifying the addKey() method as following:

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In the ItemViewer.java changes there are two new internationalization strings that are introduced.

In <dspace-src>/dspace-xmlui/src/main/webapp/i18n/messages.xml add the following strings:k4'**$2' I'5]^W4&,1U90.13$-.@0/T*'0UC.'4i1'T'0U$))0$.1(2^ l9)) 7$.1(2kO4'**$2'l

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The next task to implement is the form processing for adding a rating. This will require adding a

mapping pattern for the add-ratings page that will call

a AddRatingsAction class. This AddRatingsAction class will add/update a sd to the database and

redirects the user back to the item display page. In <dspace-src>/dspace-

xmlui/src/main/resources/aspects/ViewArtifacts/sitemap.xmap add the following action to map:actions: k4$GS$-.1/( ($4']^9))7$.1(2*9-.1/(^

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In map:pipeline add the following pattern match:

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k4$GSTQ'( .'*.]^'G'0*/(^l

k4$GS4$.-Q G$..'0(]^$))!0$.1(2^l

k4$GS$-. .5G']^9))7$.1(2*9-.1/(^l

k4$GSG$0$4'.'0 ($4']^,*'01)^ R$&,']^\,*'01)cÔl

k4$GSG$0$4'.'0 ($4']^1.'41)^ R$&,']^\1.'41)cÔl

k4$GSG$0$4'.'0 ($4']^0$.1(2^ R$&,']^\0$.1(2cÔl

kO4$GS$-.l

kO4$GS4$.-Ql

kO4$GSTQ'(l

kO4$GS*'&'-.l



The AddRatingsAction class will do the following:

• Create/update rating for an item from the add-rating form.

• Redirect user back to the item display page.

In <dspace-src>/ dspace-xmlui/src/main/java/org/dspace/app/xmlui/aspect/viewArtifacts/ add a Java class

called AddRatingsAction.java :

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14G/0. /02U$G$-Q'U-/-//(U'(R10/(4'(.U7'+,'*.?

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14G/0. /02U$G$-Q'U-/-//(U'(R10/(4'(.U7')10'-./0?

14G/0. /02U$G$-Q'U-/-//(U'(R10/(4'(.U:/,0-'7'*/&R'0?

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14G/0. /02U$G$-Q'U$R$&/(U30$4'T/0IUG$0$4'.'0*UJ$0$4'.'0*?

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Conclusion

Following the implementation of the four steps as outlined a simple recommender system is now

integrated into the DSpace application using Mahout. However, there is significant potential for

the addition of other features and functions to build on this simple implementation described here.

See the list below for suggested extensions:

• Extend recommender system to support recommendations for Communities and

Collections in DSpace.

• Make the user interface look better (Some form of ratings slider and improved

recommended items list).

• Add support for dierent Similarities.

• Add support for JSPUI.

• Add support for other databases (only implemented datamodel for PostgreSQL).

• Look at solutions for the cold start problem.



• Use the ratings table that was created to generate other lists of information. for example:

highest rated item, etc.

References

1. http://mahout.apache.org/general/faq.html

2. http://www.inf.unibz.it/~ricci/papers/intro-rec-sys-handbook.pdf

3. https://www.packtpub.com/big-data-and-business-intelligence/learning-apache-mahout

Integrating a Recommender System Into DSpace

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