GRSK:

A GENERALIST RECOMMENDER SYSTEM

I. Garcia, L. Sebastia, S. Pajares and E. Onaindia

Dpt. Computer Science, Technical University of Valencia, Camino de Vera s/n, Valencia, Spain

Keywords:

Generalist recommender systems, Hybrid recommender systems, Tourism, Movies.

Abstract:

This paper describes the main characteristics of GRSK, a Generalist Recommender System Kernel. It is a

RS based on the semantic description of the domain, which allows the system to work with any domain as

long as the data of this domain can be deﬁned through an ontology representation. GRSK uses several Basic

Recommendation and Hybrid Techniques to obtain the recommended items. Through the GRSK conﬁguration

process, it is possible to select which techniques to use and to parameterize different aspects of the recommen-

dation process, in order to adjust the GRSK behavior to the particular application domain. The experimental

results will show that GRSK can be successfully used with different domains.

1 INTRODUCTION

Every day, new data appears on the Web. Everyone

browsing on the Internet can have the perception of

the huge amount of information available, which can

lead to a situation of information overload, that is the

situation where there is far too much information at

people disposal so that useful information could be

hidden by other data. In this case, techniques to re-

trieve useful information become more and more im-

portant. The usefulness of information depends on the

users and their objectives, so retrieval systems have to

try to understand the purpose of a user search in order

to propose information he could be interested in. A

special kind of information retrieval techniques that

focuses on this issue is named information ﬁltering.

As the name suggests, starting from a big set of infor-

mation, this technique identiﬁes a small subset which

should include the useful/interesting information.

Recommendation systems are a speciﬁc type

of information ﬁltering technique that attempts to

present information items (e.g. movies, songs, activi-

ties, etc.) that are likely of interest to the user. A rec-

ommender system (RS) (Resnick P., Varian H., 1997)

is personalization tool that attempts to provide people

with lists of information items that best ﬁt their indi-

vidual tastes. A RS infers the user’s preferences by

analyzing the available user data, information about

other users and information about the environment.

RS are used to either predict whether a given user

will like a particular item or identify the top N items

that will be of interest to the user. In RS, how much

a particular user likes an item is represented by a rat-

ing. Basically, a RS estimates ratings for the items

that have not been seen by a user and recommends to

the user the items with the highest estimated ratings.

Being an instance of information ﬁltering, recom-

mendation systems can be based on the demographic

ﬁltering algorithm, the content-based ﬁltering algo-

rithm or the collaborative ﬁltering algorithm

. All

these approaches have advantages and disadvantages

(Adomavicius G., Tuzhilin A., 2005); a common so-

lution adopted by many RS is to combine these tech-

niques into an hybrid RS (Pazzani M.J., 1999; Burke

R., 2007) thus improving recommendations by allevi-

ating the limitations of one technique with the advan-

tages of others.

Recently, some researchers have been focusing

on enhancing recommendations by exploiting a se-

mantic description of the domain in which recom-

mendations are provided (van Setten M., Reitsma J.,

Ebben P., 2006),(Tao Li, Anand S.S., 2009). In gen-

eral, items handled by the system are semantically de-

scribed through an ontology. Then, the recommenda-

tions are based on the semantic matching between the

user proﬁles and the item descriptions. The main dis-

advantage of these approaches is that a semantic rep-

resentation of the domain has to be available and, up

to now, user proﬁles and items are described manu-

ally.

These

algorithms will be detailed later on.

211

Garcia I., Sebastia L., Pajares S. and Onaindia E.

GRSK: A GENERALIST RECOMMENDER SYSTEM.

DOI: 10.5220/0002779302110218

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

This paper summarizes the main characteristics of

the Generalist Recommender System Kernel (GRSK).

It is a RS based in a semantic description of the do-

main that uses a hybrid recommendation technique,

fed by the recommendations obtained from different

algorithms. The task of GRSK is to generate the

list of the top N items that will be of interest to the

user. GRSK can be parameterized to adjust the sys-

tem working model, i.e. to use the desired recom-

mendation techniques. Besides, it is prepared to in-

clude as many techniques as desired by simply devel-

oping new modules. On the other hand, it is a domain-

independent engine, able to work with different cata-

logs of items to recommend.

This paper is organized as follows. Section 2 gives

an overview on the GRSK architecture, the informa-

tion GRSK needs (ontology and user information)

and, ﬁnally, the GRSK recommendation process. Sec-

tion 3 explains the process of GRSK conﬁguration to

be integrated into a system. Section 4 presents the re-

sults we have obtained when working with a tourism

domain and with a movies domain. We ﬁnish with

some conclusions and future work.

2 GRSK: GENERALIST

RECOMMENDER SYSTEM

KERNEL

2.1 GRSK Ontology

The GRSK behaviour relies on the use of a ontology

to describe the user’s preferences and the items to rec-

ommend. It has been designed to be generalist, so

GRSK is able to work with any application domain

as long as the data of the new domain can be deﬁned

through an ontology representation.

An ontology is a formal representation of a set of

concepts within a domain and the relationships be-

tween those concepts. The GRSK ontology contains

the features that describe the items in the domain. For

example, in the tourism domain, the ontology is com-

posed of terms describing architectonic styles or types

of buildings. Figure 1 shows an example of this on-

tology. In the movies domain (ﬁgure 2), the feature

denote the ﬁlm genres. It is important to remark that

GRSK is able to work from simple ontologies (such as

the movies ontology, which is basically a list of gen-

res) to more complex ontologies (with several levels

of reﬁnements, for example).

The items in the domain are described by the

features of the ontology. Moreover, each pair item-

feature is associated a value to indicate the degree

Science

Park

Museum

Open

spaces

Thematic

park

Spectacles

Science

Children

Visit to

L’Oceanografic

Visit to

L’Hemisferic

Zoo -

Aquarium

Visit to

P. Felipe M.

Visit to

Bioparc Zoo

Visit to

Turia Garden

Visit to

Cabecera Garden

Figure 1: Part of the e-Tourism ontology.

Drama

Romance

Comedy

Cinema Paradiso

Action

Thriller

Chairman of the

Board

Titanic

The firm

Figure 2: Part of the e-Movies ontology.

of interest of the item under the feature, i.e. as a

member of the category denoted by the feature. An

item can also be categorized by more than one feature

in the ontology. Formally, an item i is described by

means of a list of tuples of the form (i, f ,d

), where

f is a feature deﬁned in the ontology and d

i f

∈[0, 100]

is the degree of interest of the item i under the feature

f. Additionally, items are associated a numeric value

(acceptance counter) to represent how popular

the item i is among users; this value indicates how

many times this item has been accepted when recom-

mended.

2.2 User Information

In order to compute a recommendation, GRSK

records a proﬁle of each user, which models the user

tastes and preferences as well as his historical inter-

action with the system. The proﬁle of a given user u

records, in ﬁrst place, personal and demographic de-

tails about the user like the age, the gender, the family

or the country. Second, the user proﬁle also contains

the user general-likes model, denoted by GL

, which

is a list of the features f in the ontology the user is in-

terested in along with the user ratings r

u f

for those

features: GL

= {(u, f, r

u f

)}, where r

u f

∈ [0,100].

A user proﬁle in GRSK also contains information

about the historical interaction of the user with the

RS, namely the set of items i the user has been rec-

ommended and his degree of satisfaction r

with the

recommended items: RT

= {(u, i,r

}, where r

∈

[0,100].

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212

Basic Recommendation

Techniques (BRT)

Collaborative

Hybrid Techniques

Manager (HTM)

General likes

Content-based

Engine

Recommendation

query

Final

recommendations

Rated

recommendations

Recommendations

Ranked N

Recommendations

Mixed Hybrid RS

User

Profile

User

Preferences

Pd, Pcb, Pcol, Pgl

Weighted Hybrid

Items Selector

Preferences

Pd, Pcb, Pcol, Pgl

Recommendations

Demographic

Figure 3: GRSK Architecture.

2.3 The GRSK Architecture

Figure 3 shows an sketch of the GRSK architecture.

The Engine module is the core of GRSK. The ﬁrst

task of the Engine is to capture and store the user pro-

ﬁle when the user logs in the system for the ﬁrst time.

Then, the information obtained during the interaction

of the user with the system after the recommendation

(rated recommendations) will be used to update his

proﬁle to better capture his preferences.

The Engine is also in charge of controlling the rec-

ommendation process, which consists of two steps:

ﬁrst, each basic recommendation technique calculates

a set of preferences for the user proﬁle; and then, the

items selector obtains the items that match the user

preferences which are combined by the hybrid tech-

nique to obtain the ﬁnal list of recommended items.

The modules used by the engine to obtain the recom-

mendation are:

• Basic Recommendation Techniques (BRT)

(Burke R., 2007) (demographic RS, content-based

RS, collaborative RS and general likes-based

ﬁltering) are used to obtain the user preferences

by analyzing his own proﬁle, the proﬁles of other

users and the items selected by the users that

have utilized the system before. For a given user,

each BRT creates a different list of preferences

according to the parameters and data handled

by the technique. The system conﬁguration

allows to select the set of BRT to use in the

recommendation process (see section 3).

• Items Selector: receives the lists of user prefer-

ences and, for each list, it returns the set of items

that better match the elements in the list.

• The Hybrid Techniques Manager (HTM) com-

bines the lists of items in a single list, that con-

form the ﬁnal user recommendation list. The hy-

brid techniques are applied on items, not on pref-

erences. At this moment, GRSK includes two hy-

brid recommendation methods: the mixed hybrid

technique and the weighted hybrid technique. The

system conﬁguration allows to select only one hy-

brid technique to use in the recommendation pro-

cess.

At this moment, GRSK includes several BRT and

two hybrid techniques, but it is prepared to work with

as many techniques as desired by simply developing

new modules. We opted for these techniques because

we considered them more suitable for the most com-

mon domains.

2.4 GRSK Recommendation Process

The recommendation process in GRSK is divided in

two steps. The ﬁrst one is to obtain the preferences

that deﬁne the items that will be of interest to the user

(section 2.4.1). The user introduces his query, which

is sent joint with his proﬁle to the BRT to produce a

list of individual preferences for each technique. The

second step is to obtain the list of items to recommend

(section 2.4.2). This second step includes to obtain

the list of items that match the preferences and to ap-

ply an hybrid recommendation technique to obtain the

ﬁnal ranked list of recommended items.

2.4.1 Modeling of User Preferences

This step consists of analyzing the user proﬁle and

eliciting the corresponding list of preferences. It is

important to note that, unlike most RS, GRSK is a se-

mantic RS that does not initially work with the items

that will be later recommended to the user. In con-

trast, GRSK makes use of the concept of feature to

elicit the user preference model, which is a more gen-

eral and ﬂexible entity. This makes GRSK able to

work with any application domain as long as the data

can be represented through an ontology.

A preference (which is a tuple of the form

(u, f, d

)) is a feature f in the ontology with a

interest-degree of d

u f

for a user u, selected by one

of the four basic recommendation techniques: de-

mographic recommendation, content-based recom-

mendation, collaborative recommendation and gen-

eral likes-based ﬁltering. Each BRT generates a dif-

ferent set of preferences, an independent list of pref-

erences and hence the lists may contain different fea-

tures or the same feature with different degrees of in-

terest. We will call these lists P

for the demographic

preference list, P

for the content-based preference

list, P

col

for the collaborative preference list, and P

for the general-likes-based preference list.

GRSK: A GENERALIST RECOMMENDER SYSTEM

213

The demographic BRT classiﬁes the user into

a demographic category according to his proﬁle de-

tails. Each demographic category is associated a

list of preferences (P

) during the system conﬁgura-

tion because they depend on the application domain.

The success of the demographic recommendation is

strongly dependant of this user classiﬁcation. We

opted for a demographic BRT because it is a good

alternative to solve the problem of the new user since

it is able to always give a recommendation.

The content-based RS technique computes a set

of preferences by taking into account the items previ-

ously rated by the user (historical interaction). This

technique will allow us to increase the user satisfac-

tion by recommending items similar to those already

accepted by the user. Let f be a feature and I a list of

items described under the feature f in the ontology; I

will be a list of tuples of the form (i, f ,d

) for a par-

ticular feature f . Let RT

= {(u, i,r

)} be the set of

items valued by user u with respective ratings of r

;

a preference (u, f, d

u f

) is added to the list P

where:

∑

∀i∈I∩RT

i f

∗r

|RT

The value d

denotes the interest-degree of a user

for the items described under the feature f amongst

the whole set of items rated by the user.

The collaborative RS technique suggests those

items preferred by people with a proﬁle most simi-

lar to the given user proﬁle (i.e. the user will be rec-

ommended items that people with similar tastes and

preferences liked in the past). This technique is only

useful when there is a great amount of data concern-

ing items rated by other users. In order to obtain the

corresponding list of preferences P

col

, this technique

decides whether a user v is similar to the given user

u (s

u,v

) by applying the Pearson Correlation with re-

spect to the items that have been rated by both users.

Then, by taking into account all the users v similar to

u, a preference (u, f, d

) is added to P

col

for each f

that describes an item i rated by v, where:

u f

= avg(d

i f

∗r

),∀v : s

u,v

The general-likes-based ﬁltering is an informa-

tion ﬁltering technique that obtains the preferences

that match with the main user interests speciﬁed by

the user in his proﬁle (GL

). The accuracy of this

technique depends on the information provided by the

user. However, GRSK is able to work with few infor-

mation. In this case, the set of preferences P

is sim-

ply built as P

= GL

; that is, the interest-degree of

the preferences in P

will be the ratings given by the

user to that particular feature in his proﬁle (d

= r

2.4.2 Obtention of the List of Recommended

Items

In the second step of the recommendation process,

the Items Selector selects, among all of the items

in the domain, those that best match the preferences

in the lists P

, P

col

and P

. Afterwards, the

selected Hybrid Technique obtains a single list of

ranked recommendations that we will denote as RI

{(u,i,d

)}, where i is the item, and d

is the esti-

mated interest-degree of the item i for the user u.

The method for selecting an item is quite simple:

an item i represented by the tuple (i, f, d

i f

) matches

a preference in P

brt

if there is a tuple (u, f,d

u f

) in

brt

such that the item has not previously rated by the

user. The outcome of the Items Selector is a set of

lists of ranked items, one list per BRT. The lists of

recommended items computed by the Items Selector

are then processed by the selected Hybrid Technique

and returns a single list of ranked items (RI

). The

value d

of a tuple in RI

depends on the selected Hy-

brid Technique. At this moment, GRSK includes two

hybrid techniques: mixed and weighted techniques.

The Mixed Hybrid Technique mixes the items in

the lists of all the BRT. All items are handled in the

same way with independency the BRT they belong to.

In this case, the value d

of a tuple in RI

is calculated

as follows:

= percentile(AC

) + avg

∀f

i f

+ d

u f

)

where percentile(AC

) refers to the percentile of

the acceptance counter of i (AC

) with respect to the

whole set of items rated by the users. The second part

of the formula considers the average interest-degree

of all the features that describe the item i in both the

ontology (d

i f

) and in the user preferences (d

The Weighted Hybrid Technique mixes the

items in the lists, but the value d

is computed ac-

cording to the weight of the BRT that selected the

preference for which the item has been recommended.

The weight of each BRT, deﬁned in the conﬁguration

process, is denoted by ω

for the demographic RS,

for the content-based RS, ω

col

for the collabora-

tive RS and ω

for the general-likes ﬁltering. In this

case the value d

of a tuple in RI

is calculated as:

= percentile(AC

) + avg

∀f

((d

+ d

) ×ω

brt

)

The Hybrid Technique obtains a list of ranked

items and retrieves the best N ranked elements.

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214

GRSK Subsystem

User Interface Subsystem

User Profile

Recommended

Items

User info

Rated items

Recommended

items

Generalistic Database Interface

Subsystem

Items Users Profiles Ontology

Users

Classification

GRSK

Setup

Rated items

Figure 4: GRSK Integration into a System.

3 GRSK INTEGRATION

PROCESS

This section describes the GRSK requirements to be

integrated into any system (ﬁgure 4).

3.1 Database and External Subsystems

In ﬁrst place, GRSK needs a database of the particu-

lar application domain containing: (1) the domain on-

tology; (2) the set of items that can be recommended:

these items must be classiﬁed according to the ontol-

ogy and the quality of the recommendation depends,

in part, on the accuracy of the classiﬁcation of items;

(3) the user proﬁles with the demographic user infor-

mation (if the demographic RS is used in GRSK) and

the user general likes GL

(if the general-likes ﬁlter-

ing is used): the quality of the recommendation also

depends on the information provided by the user - the

more information, the more accurate the recommen-

dation -, but it is possible to obtain a recommendation

with a minimum amount of data; (4) demographic

classiﬁcation of users according to the ontology.

In order to obtain a complete recommender sys-

tem, two external modules must be plugged to GRSK:

the Database Interface and the User Interface. The

Database Interface subsystem, which is the inter-

face between GRSK and the database, processes the

queries coming from GRSK, such as obtaining the

user proﬁle of the current user or the list of items

that match a given preference. On the other hand,

the User Interface Subsystem initiates the execution

of GRSK and centralizes the exchange of information

between the user and GRSK. This includes convert-

ing the user data into a user proﬁle, showing the list

of recommended items and recording the ones that are

selected and discarded by the user joint with the rat-

ing of the user satisfaction with a given recommended

item. The User Interface Subsystem is also in charge

of deciding which information must be initially intro-

duced by the user (which depends on the particular

application domain).

3.2 GRSK Setup

GRSK requires an initial conﬁguration to adjust the

GRSK behaviour to the current application. First, it

is possible to select which BRT among all the avail-

able BRT (demographic RS, content-based RS, col-

laborative RS and general likes-based ﬁltering), will

be used in GRSK to give a recommendation. Second,

it is necessary to select only one hybrid recommenda-

tion technique. Moreover, for all hybrid techniques, it

is possible to select the way to compute the interest-

degree of items in case an item is selected by more

than one preference. The techniques are: maximum

ratio, median ratio and several techniques to compute

the average.

On the other hand, some other computations can

be parameterized. For example, a threshold of the

interest-degree can be deﬁned to consider or not a

given preference. Or the acceptance counter can be

computed in several ways.

4 CASE STUDIES

This subsection discusses the experiments conducted

to evaluate the behavior of GRSK.

Two domains have been used to evaluate GRSK: a

tourism domain and a movies domain. Through these

case studies, we will show that GRSK has been suc-

cessfully used in both cases.

In order to test GRSK, we selected two classical

Information Retrieval metrics: precision and recall.

In an Information Retrieval scenario, precision is de-

ﬁned as the number of retrieved relevant items divided

by the total number of items retrieved by that search;

and recall is deﬁned as the number of retrieved rel-

evant items divided by the total number of existing

relevant items. That is, precision represents the prob-

ability that a retrieved item is relevant to the user and

recall is the probability that a relevant item is retrieved

by the search.

Speciﬁcally, we call Ns the number of retrieved

items by GRSK, that is, the number of recommenda-

tions solicited by the user. The number of relevant

items is denoted by Nr and Nrs is the number of rel-

evant items retrieved in the recommendation, that is,

GRSK: A GENERALIST RECOMMENDER SYSTEM

215

Nrs = Nr ∩Ns. Then, precision and recall are calcu-

lated as follows:

P =

Nrs

R =

Nrs

Often, there is an inverse relationship between P

and R, where it is possible to increase one at the cost

of reducing the other. For example, R can be in-

creased by increasing Ns, at the cost of increasing the

number of irrelevant items retrieved (decreasing P).

For this reason, P and R ratios are not discussed in

isolation.

We run our experiments in terms of two param-

eters, Ns the number of retrieved items, and the in-

formation about past visits in the user proﬁle. As for

Ns, we run tests with Ns = 10 and Ns = 25. In both

experiments, we obtained the same list of retrieved

items, but in the ﬁrst case, the system considered the

ﬁrst 10 items and, in the second case, the ﬁrst 25 items

were considered. Regarding the second parameter, we

took into account four levels of historical information

in the user proﬁle; a new user and user proﬁles that

store 25%, 50% and 75% of (randomly selected) rated

items, respectively.

4.1 e-Tourism, a Touristic

Recommender System

e-Tourism is a web-based recommender system that

computes a user-adapted leisure and tourist plan for a

given user. The system does not solve the problem of

traveling to an speciﬁc place but it works on recom-

mending a list of the activities that a tourist can per-

form in the city of Valencia (Spain). It also computes

a time schedule for the list of recommended activi-

ties taking into account the distance between places,

the opening hours, etc. - that is, an agenda of ac-

tivities (Sebastia L., Garcia I., Onaindia E., Guzman

C., 2009). It is intended to be a service for foreigners

and locals to become deeply familiar with the city and

plan leisure activities.

4.1.1 Data Warehouse e-Tourism

As this is a new domain, a survey was ﬁlled by 58

people in order to obtain data for testing the system.

Personal data like name, age, marital status and tourist

proﬁle (cultural, business, family, etc.) were col-

lected. They also identiﬁed sites already visited along

with a degree of interest (rating) for each site. There

are 115 preferences structured in the ontology (see

ﬁgure 1), 141 sites stored and 58 user proﬁles. Each

user rated (positively or negatively) all sites (RT

) and

an average of 110 preferences (GL

Figure 5: Comparison of the P and R values obtained when

Ns=10 and Ns=25 and for the four degrees of historical in-

formation for the tourism domain.

4.1.2 e-Tourism Experimental Results

When performing the experimental results in this do-

main, we divided the user proﬁles database into two

sets: 48 users were the training users and 10 users

were the test users. Then, as all users rated all sites,

we considered as relevant items (Nr) those visits that

the test users marked as visited with a positive degree

of satisfaction in the survey.

Figure 5 shows a comparison between the aver-

age of precision (P) and recall (R) for all the different

cases of user feedback. When Ns = 10, the difference

between the precision and the recall is remarkable,

and the precision decreases as the recall increases, as

expected. However, when Ns = 25, this difference is

not so noticeable. When Ns = 10 and the information

provided to the system increases (H = 25, H = 50),

GRSK improves the quality of the recommendations

if we consider P and R together. However, in some

of the cases in which the user feedback is rather high

(H = 75), the quality of the recommendation wors-

ens. This is because the database does not contain a

large number of items and, therefore, GRSK is not

able to recommend places other than those ones al-

ready visited by the user. When Ns = 25, the general

impression is similar. However, in this case, the rela-

tion P −R is better because, although the precision is

a bit lower, the recall increases in a higher order. Here

again, the more feedback, the better the quality of the

recommendation, and, unlike the previous case, the

worsening in the case of H = 75 is not as noticeable.

4.2 e-Movies: a Movies Recommender

System

e-Movies is a application-based recommender sys-

tem that computes user tastes regarding preferences

movies for a given user, in order to obtain the best list

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

216

of movies for the user. It is intended to be a service for

any cinephile, working with a multitude of movies.

4.2.1 Data Warehouse e-Movies

In this case, we selected a well-known movies

database, MoviLens

, which has been created by the

GroupLens research group at the University of Min-

nesota. It contains 900 user proﬁles with their respec-

tive histories of interaction with the system and a set

of 1682 ﬁlms. A user has scored between 20 and 700

movies. Each ﬁlm is described by a title, number of

people who were recommended the ﬁlm and watched

it, the year was recorded, etc. All the ﬁlms have been

cataloged through an ontology of twenty preferences

(see ﬁgure 2). Each user has an average of 15 pref-

erences associated with several ratios (GL

) and has

rated an average of 45 movies (RT

). Moreover, each

movie has been rated by 57 different users in average

and has been described by 2 preferences in average.

4.2.2 e-Movies Experimental Results

When performing the experimental results in this do-

main, we divided the user proﬁles database into two

sets: 890 users were the training users and 10 users

were the test users. We considered as relevant items

(Nr) those movies that the test users have marked with

a value between 2 and 5.

Figure 6 shows a comparison between the aver-

age of precision (P) and recall (R) for all the different

cases of user feedback. In all cases, the difference

between the precision and the recall is quite remark-

able. The reason behind is that the number of relevant

items (Nr) is quite high compared to the number of

retrieved items as each test user has rated up to 685

movies and has an average of 551 movies. On the

other hand, we expected (as in the tourism domain)

that both measures (considered together) increased as

the user history also increased (except when H=75, as

explained above). However, ﬁgure 6 shows that when

H = 50 the precision decreases slightly. The reason is

the following. The precision P is calculated by tak-

ing into account a user history. Remember that the

possibility that a retrieved relevant item in Nrs was

not included in this Nr is not considered in P, there-

fore it must be satisﬁed that Nrs ⊆ Nr. Thereby the

more feedback level, the lower Nr is observed, being

Ns constant. This is the reason why P with 25% is a

little bit better than P with 50%, because it is easier to

ﬁnd a retrieved relevant item within the 75% user his-

tory (25% feedback) than with 50%. If we could ask

the user about his satisfaction with respect to a given

http://www.grouplens.org/

Figure 6: Comparison of the P and R values obtained when

Ns=10 and Ns=25 and for the four degrees of historical in-

formation for the movies domain.

recommendation, we would have a better picture of

the GRSK performance in this domain. This does not

happen in the tourism domain because we have a com-

plete feedback for all users. We also have the intuition

that a more complex ontology and a more complete

description of items (such as in the tourism domain)

improves the quality of the recommendations. How-

ever, we need to perform further experiments to con-

ﬁrm this intuition.

5 RELATED WORK

Some general-purpose domain independent open

source libraries and engines have been developed in

order to reuse the effort to design recommender sys-

tems. Some of these systems are: RACOFI (Ander-

son M., Ball M., Boley H., Greene S., Howse N.,

Lemire D., McGrath S., 2003), SUGGEST (Desh-

pande M., Karypis G., 2004), Vogoo (Lemire D., Mc-

Grath S., 2005), Taste

, CoFE (Ogston E., Bakker A.,

van Steen M., 2006), ColFi (Brozovsky L., 2006),

Duine Toolkit (van Setten M., Reitsma J., Ebben P.,

2006) and Aura (Lamere P., Green S., 2008).

Most of these engines are Java-based, with the ex-

ception of SUGGEST (C) and Vogoo (PHP). At this

moment, there are two versions of GRSK, written in

Java and in C#. GRSK Java version is agent-based, as

RACOFI and Aura.

Duine Toolkit, RACOFI and ColFi are developed

with a modular architecture that allows developers to

change and add algorithms easily, in the same manner

than GRSK. The GRSK conﬁguration process allows

to select which techniques to use and to parameterize

different aspects of the recommendation process, in

order to adjust the GRSK behavior to the particular

http://www.opentaste.net/

GRSK: A GENERALIST RECOMMENDER SYSTEM

217

application domain.

Most of these systems are collaborative recom-

mendation engines (ColFi, Coﬁ, Taste, SUGGEST

and Vogoo). RACOFI, Aura and Duine Toolkit are hy-

brid recommendation engines. RACOFI adjusts a col-

laborative ﬁlter prediction with mechanisms coming

from content-based approaches. Aura uses collabora-

tive recommendation but uses a mechanism that as-

signs and processes a set of tags to items to improve

the recommendation. Duine Toolkit uses collabora-

tive and content-based techniques.

GRSK is an hybrid recommendation engine that

employs different basic and hybrid recommendation

techniques. The purpose of including these different

recommendation techniques is to make GRSK able

to work with any application domain, independently

from the number of users, the available user informa-

tion, etc. On the other hand, it is based on the seman-

tic description of the items in the domain.

6 CONCLUSIONS AND FURTHER

WORK

This paper describes the main characteristics of

GRSK, a Generalist Recommender System Kernel.

It is a RS based on the semantic description of the

domain, which allows the system to work with any

domain as long as the data of this domain can be

deﬁned through an ontology representation. GRSK

uses four Basic Recommendation Techniques (de-

mographic, content-based, collaborative and general

likes ﬁltering) and two disjunctive Hybrid Techniques

(mixed and weighed) that join the recommendations

obtained from each BRT. Through the GRSK conﬁg-

uration process, it is possible to select which tech-

niques to use and to parameterize different aspects

of the recommendation process, in order to adjust the

GRSK behavior to the particular application domain.

The experimental results show that GRSK can be suc-

cessfully used with different domains.

Now we are working in the extension of GRSK to

group recommendation (Garcia I., Sebastia L., Onain-

dia E., Guzman C., 2009). We are developing differ-

ent innovative techniques to compute the group pro-

ﬁle (such as the Incremental Intersection Technique).

In order to get closer the process of creating the group

proﬁle to human behaviour, we are using agreement

techniques. More speciﬁcally, we are working on a

protocol of alternative offers between the group mem-

bers to obtain the preferences that will compose the

group proﬁle.

ACKNOWLEDGEMENTS

Partial support provided by Consolider Ingenio

2010 CSD2007-00022, Spanish Government Project

MICINN TIN2008-6701-C03-01 and Valencian Gov-

ernment Project Prometeo 2008/051.

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