=Paper=
{{Paper
|id=Vol-532/paper-4
|storemode=property
|title=A Tag Recommender System Exploiting User and Community Behavior
|pdfUrl=https://ceur-ws.org/Vol-532/paper4.pdf
|volume=Vol-532
}}
==A Tag Recommender System Exploiting User and Community Behavior==
https://ceur-ws.org/Vol-532/paper4.pdf
A Tag Recommender System Exploiting
User and Community Behavior
Cataldo Musto Fedelucio Narducci Marco De Gemmis
Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science
University of Bari ‘Aldo Moro’ University of Bari ‘Aldo Moro’ University of Bari ‘Aldo Moro’
Italy Italy Italy
cataldomusto@di.uniba.it narducci@di.uniba.it degemmis@di.uniba.it
Pasquale Lops Giovanni Semeraro
Dept. of Computer Science Dept. of Computer Science
University of Bari ‘Aldo Moro’ University of Bari ‘Aldo Moro’
Italy Italy
lops@di.uniba.it semeraro@di.uniba.it
ABSTRACT Information filtering
Nowadays Web sites tend to be more and more social: users
can upload any kind of information on collaborative plat- General Terms
forms and can express their opinions about the content they
Algorithms, Experimentation
enjoyed through textual feedbacks or reviews. These plat-
forms allow users to annotate resources they like through
freely chosen keywords (called tags). The main advantage Keywords
of these tools is that they perfectly fit user needs, since the Recommender Systems, Web 2.0, Collaborative Tagging Sys-
use of tags allows organizing the information in a way that tems, Folksonomies
closely follows the user mental model, making retrieval of
information easier. However, the heterogeneity characteriz-
ing the communities causes some problems in the activity of 1. INTRODUCTION
social tagging: someone annotates resources with very spe- We are assisting to a transformation of the Web towards
cific tags, other people with generic ones, and so on. These a more user-centric vision called Web 2.0. By using Web 2.0
drawbacks reduce the exploitation of collaborative tagging applications users are able to publish auto-produced con-
systems for retrieval and filtering tasks. Therefore, systems tents such as photos, videos, political opinions, reviews, hence
that assist the user in the task of tagging are required. The they are identified as Web prosumers: producers + consumers
goal of these systems, called tag recommenders, is to suggest of knowledge. Recently the research community has thor-
a set of relevant keywords for the resources to be annotated. oughly analyzed the dynamics of tagging, which is the act
This paper presents a tag recommender system called STaR of annotating resources with free labels, called tags. Many
(Social Tag Recommender system). Our system is based on argue that, thanks to the expressive power of folksonomies
two assumptions: 1) the more two or more resources are sim- [17], collaborative tagging systems are very helpful to users
ilar, the more they share common tags 2) a tag recommender in organizing, browsing and searching resources. This hap-
should be able to exploit tags the user already used in order pens because, in contrast to systems where information about
to extract useful keywords to label new resources. We also resources is only provided by a small set of experts, the
present an experimental evaluation carried out using a large model of collaborative tagging systems takes into account
dataset gathered from Bibsonomy. the way individuals conceive the information contained in a
resource [18], so they perfectly fit user needs and user mental
model. Nowadays almost all Web 2.0 platforms embed tag-
Categories and Subject Descriptors ging: we can cite Flickr1 , YouTube2 , Del.icio.us3 , Last.fm4 ,
H.3.1 [Information Storage and Retrieval]: Content Bibsonomy5 and so on. These systems provide heteroge-
Analysis and Indexing: Indexing methods; H.3.3 [Information neous contents (photos, videos, musical habits, etc.), but
Storage and Retrieval]: Information Search and Retrieval: they all share a common core: they let users to post new re-
sources and to annotate them with tags. Besides the simple
act of annotation, the tagging of resources has also a key so-
cial aspect; the connection between users, resources and tags
generates a tripartite graph that can be easily exploited to
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analyze the dynamics of collaborative tagging systems. For The paper is organized as follows. Section 2 analyzes re-
example, users that label the same resource by using the lated work. Section 3 explains the architecture of the system
same tags might have similar tastes and items labeled with and how the recommendation approach is implemented. The
the same tags might share common characteristics. experimental evaluation carried out is described in Section
Undoubtedly the power of tagging lies in the ability for 4, while conclusions and future work are drawn in the last
people to freely determine the appropriate tags for a re- section.
source [10]. Since folksonomies do not rely on a predefined
lexicon or hierarchy they have the main advantage to be 2. RELATED WORK
fully free, but at the same time they generate a very noisy
Usually the works in the tag recommendation area are
tag space, really hardly to exploit for retrieval or recom-
broadly divided into three classes: content-based, collabora-
mendation tasks without performing any form of processing.
tive and graph-based approaches.
Golder et. al. [4] identified three major problems of col-
In the content-based approach, exploiting some Informa-
laborative tagging systems: polysemy, synonymy, and level
tion Retrieval-related techniques, a system is able to ex-
variation. Polysemy refers to situations where tags can have
tract relevant unigrams or bigrams from the text. Brooks et.
multiple meanings: for example a resource tagged with the
al [2], for example, develop a tag recommender system that
term bat could indicate a news taken from an online sport
exploits TF/IDF scoring [13] in order to automatically sug-
newspaper or a Wikipedia article about nature. We refer to
gests tags for a blog post. In [5] is presented a novel method
synonymy when multiple tags share a single meaning: for
for key term extraction from text documents. Firstly, ev-
example we can have simple morphological variations (such
ery document is modeled as a graph which nodes are terms
as ’AI’,’artificial intelligence’ and so on to identify a scien-
and edges represent semantic relationship between them.
tific publication about Artificial Intelligence) but also lexical
These graphs are then partitioned using communities de-
relations (like resources tagged with ‘arts’ versus ‘cultural
tection techniques and weighted exploiting information ex-
heritage’). At last, the phenomenon of tagging at different
tracted from Wikipedia. The tags composing the most rel-
levels of abstraction is defined as level-variation. This hap-
evant communities (a set of terms related with the topic of
pens when people annotate the same web page, containing
the resource) are then suggested to the user.
for example a recipe for roast turkey with the tag ‘roast-
AutoTag [11] is one of the most important systems imple-
turkey’ but also with a simple ‘recipe’.
menting the collaborative approach for tag recommendation.
Since these problems are of hindrance to completely ex-
It presents some analogies with collaborative filtering meth-
ploit the expressive power of folksonomies, in the last years
ods: as in the collaborative recommender systems the rec-
many tools have been developed to assist the user in the
ommendations are generated based on the ratings provided
task of tagging and to aid at the same time the tag con-
by similar users (called neighbors), in AutoTag the system
vergence [3]: we refer to them as tag recommenders. These
suggests tags based on the other tags associated with sim-
systems work in a very simple way:
ilar posts. Firstly, the tool exploits some IR techniques in
1. a user posts a resource; order to find similar posts and extracts the tags they are
annotated with. All the tags are then merged, building a
2. depending on the approach, the tag recommender ana- folksonomy that is filtered and re-ranked. The top-ranked
lyzes some information related to the resource (usually tags are finally suggested to the user, who selects the most
metadata or a subset of the relations in the aforemen- appropriate ones to attach to the post.
tioned tripartite graph); TagAssist [15] extends the AutoTags’ approach introduc-
ing some preprocessing step (specifically, a lossless compres-
3. the tag recommender processes this information and sion over existing data) in order to improve the quality of
produces a list of recommended tags; the recommendations. The core of this approach is repre-
4. the user freely chooses the most appropriate tags to sented by a a Tag Suggestion Engine (TSE) which leverages
annotate the resource. previously tagged posts providing appropriate suggestions
for new content.
Clearly, the more these recommended tags match the user Marinho [9] investigates the user-based collaborative ap-
needs and her mental model, the more she will use them to proach for tag recommendation. The main outcome of this
annotate the resource. In this way we can rapidly speed up work shows that users with a similar tag vocabulary tend to
the tag convergence aiding at the same time in filtering the tag alike, since the method seems to produce good results
noise of the complete tag space. when applied on the user-tag matrix.
This paper presents the tag recommender STaR. When de- The problem of tag recommendation through graph-based
veloping the model, we tried to point out two concepts: approaches has been firstly addressed by Jäschke et al. in [7].
The key idea behind their FolkRank algorithm is that a re-
• resources with similar content should be annotated source which is tagged by important tags from important
with similar tags; users becomes important itself. So, they build a graph
• a tag recommender needs to take into account the pre- whose nodes mutually reinforces themselves by spreading
vious tagging activity of users, increasing the weight their weights. They compared some recommendation tech-
of the tags already used to annotate similar resources. niques including collaborative filtering, PageRank and FolkRank,
showing that the FolkRank algorithm outperforms other ap-
In this work, we identify two main aspects in the tag rec- proaches. Furthermore, Schmitz et al. [14] proposed associ-
ommendation task: first, each user has a typical manner ation rule mining as a technique that might be useful in the
to label resources; second, similar resources usually share tag recommendation process.
common tags. In literature we can find also other methods (called hy-
brid ), which try to integrate two of more sources of knowl- users bookmarked the same resource, they will receive
edge (mainly, content and collaborative ones) in order to the same suggestions since the folksonomies built from
improve the quality of recommended tags. similar resources are the same.
Heymann et. al [6] present a tag recommender that ex-
ploits at the same time social knowledge and textual sources. We will try to overcome these drawbacks, by proposing an
They produce recommendations exploiting both the HTML approach firstly based on the analysis of similar resources ca-
source code (extracting anchor texts and page texts) and the pable also of leveraging the tags already selected by the user
annotations of the community. The effectiveness of this ap- during her previous tagging activity, by putting them on the
proach is also confirmed by the use of a large dataset crawled top of the tag rank. Figure 1 shows the general architecture
from del.icio.us for the experimental evaluation. of STaR. The recommendation process is performed in four
Lipczak in [8] proposes a similar hybrid approach. Firstly, steps, each of which is handled by a separate component.
the system extracts tags from the title of the resource. Af- 3.1 Indexing of Resources
terwards, it performs an analysis of co-occurrences, in or-
der to expand the sets of candidate tags with the tags that Given a collection of resources (corpus) with some textual
usually co-occur with terms in the title. Finally, tags are metadata (such as the title of the resource, the authors, the
filtered and re-ranked exploiting the informations stored in description, etc.), STaR firstly invokes the Indexer module
a so-called ”personomy”, the set of the tags previously used in order to perform a preprocessing step on these data by
by the user. exploiting Apache Lucene6 . Obviously, the kind of metadata
Finally, in [16] the authors proposed a model based on to be indexed is strictly dependant on the nature of the
both textual contents and tags associated with the resource. resources. For example, supposing to recommend tags for
They introduce the concept of conflated tags to indicate a set bookmarks, we could index the title of the web page and the
of related tags (like blog, blogs, ecc.) used to annotate a re- extended description provided by users, while for BibteX
source. Modeling in this way the existing tag space they are entries, we could index the title of the publication and the
able to suggest various tags for a given bookmark exploiting abstract. Let U be the set of users and N the cardinality
both user and document models. of this set, the indexing procedure is repeated N + 1 times:
we build an index for each user (Personal Index ) storing the
information on the resources she previously tagged and an
3. STAR: A SOCIAL TAG RECOMMENDER index for the whole community (Social Index ) storing the
SYSTEM information about all the tagged resources by merging the
Following the definition introduced in [7], a folksonomy singles Personal Indexes.
can be described as a triple (U, R, T ) where: Following the definitions presented above, given a user
u ∈ U we define P ersonalIndex(u) as:
• U is a set of users;
• R is a set of resources; P ersonalIndex(u) = {r ∈ R|∃t ∈ T : tas(u, r) = t} (1)
• T is a set of tags. where tas is the tag assignment function tas: U × R → T
which assigns tags to a resource annotated by a given user.
We can also define a tag assignment function tas: U ×
SocialIndex represents the union of all the user personal in-
R → T.
dexes:
So, a collaborative tagging system is a platform composed [
N
of users, resources and tags that allows users to freely assign SocialIndex = P ersonalIndex(ui) (2)
i=1
tags to resources, while the tag recommendation task for a
given user u ∈ U and a resource r ∈ R can be described as 3.2 Retrieval of Similar Resources
the generation of a set of tags tas(u, r) ⊆ T according to
some relevance model. In our approach these tags are gen- Next, STaR can take into account users requests in or-
erated from a ranked set of candidate tags from which the der to produce personalized tag recommendations for each
top n elements are suggested to the user. resource. First, every user has to provide some information
STaR (Social Tag Recommender) is a content-based tag rec- about the resource to be tagged, such as the title of the Web
ommender system, developed at the University of Bari. The page or its URL, in order to crawl the textual metadata as-
inceptive idea behind STaR is to improve the model imple- sociated on it.
mented in systems like TagAssist [15] or AutoTag [11]. Next, if the system can identify the user since she has
Although we agree that similar resources usually share already posted other resources, it exploits data about her
similar tags, in our opinion Mishne’s approach presents two (language, the tags she uses more, the number of tags she
important drawbacks: usually uses to annotate resources, etc.) in order to refine
the query to be submitted against both the Social and Per-
1. the tag re-ranking formula simply performs a sum of sonal indexes stored in Lucene. We used as query the title
the occurrences of each tag among all the folksonomies, of the web page (for bookmarks) or the title of the publica-
without considering the similarity with the resource to tion (for BibTeX entries). Obviously before submitting the
be tagged. In this way tags often used to annotate query we processed it by deleting not useful characters and
resources with a low similarity level could be ranked punctuation.
first; In order to improve the performances of the Lucene Query-
ing Engine we replaced the original Lucene Scoring function
2. the proposed model does not take into account the
6
previous tagging activity performed by users. If two http://lucene.apache.org
Figure 1: Architecture of STaR
with an Okapi BM25 implementation7 . BM25 is nowadays
considered as one of the state-of-the art retrieval models by
the IR community [12].
Let D be a corpus of documents, d ∈ D, BM25 returns
the top-k resources with the highest similarity value given
a resource r (tokenized as a set of terms t1 . . . tm ), and is
defined as follows:
sim(r, d) =
Pm nr
ti
i=1 k1 ((1−b)+b∗l)+nr ∗ idf (ti ) (3)
t i
where nrti represents the occurrences of the term ti in the
document d, l is the ratio between the length of the resource
and the average length of resources in the corpus. Finally, k1
and b are two parameters typically set to 2.0 and 0.75 respec-
tively, and idf (ti ) represents the inverse document frequency
of the term ti defined as follows:
N + df (ti ) + 0.5
idf (ti ) = log (4)
df (ti ) + 0.5
Figure 2: Retrieval of Similar Resources
where N is the number of resources in the collection and
df (ti ) is the number of resources in which the term ti occurs.
Given user u ∈ U and a resource r, Lucene returns the
PersonalIndex, instead, returns another online newspaper
resources whose similarity with r is greater or equal than
(Tuttosport.com). The similarity score returned by Lucene
a threshold β. To perform this task Lucene uses both the
has been normalized.
PersonalIndex of the user u and the SocialIndex. More for-
mally:
3.3 Extraction of Candidate Tags
The role of the Tag Extractor is to produce as output
P Res(u, q) = {r ∈ P ersonalIndex(u)|sim(q, r) ≥ β} the list of the so-called ”candidate tags” (namely, the tags
considered as ’relevant’ by the tag recommender). In this
step the system gets the most similar resources returned
S Res(q) = {r ∈ SocialIndex|sim(q, r) ≥ β} by the Apache Lucene engine and builds their folksonomies
(namely, the tags they have been annotated with). Next, it
Figure 2 depicts an example of the retrieving step. In
produces the list of candidate tags by computing for each
this case the target resource is represented by Gazzetta.it,
tag from the folksonomy a score obtained by weighting the
one of the most famous Italian sport newspaper. Lucene
similarity score returned by Lucene with the normalized oc-
queries the SocialIndex and returns as the most similar re-
currence of the tag. If the Tag Extractor also gets the list of
sources an online newspaper (Corrieredellosport.it) and the
the most similar resources from the user PersonalIndex, it
official web site of an Italian Football Club (Inter.it). The
will produce two partial folksonomies that are merged, as-
7
http://nlp.uned.es/ jperezi/Lucene-BM25/ signing a weight to each folksonomy in order to boost the
tags previously used by the user.
Formally, for each query q (namely, the resource to be Table 1: Results comparing the Lucene original scor-
tagged), we can define a set of tags to recommend by build- ing function with BM25
ing two sets: candT agsp and candT agss . These sets are Scoring Resource Pr Re F1
defined as follows:
Original bookmark 25.26 29.67 27.29
BM25 bookmark 25.62 36.62 30.15
candT agsp (u, q) = {t ∈ T |t = T AS(u, r) ∧ r ∈ P Res(u, q)}
Original bibtex 14.06 21.45 16.99
BM25 bibtex 13.72 22.91 17.16
candT agss (q) = {t ∈ T |t = T AS(u, r) ∧ r ∈ S Res(q) ∧ u ∈ U }
Original overall 16.43 23.58 19.37
In the same way we can compute the relevance of each tag
BM25 overall 16.45 26.46 20.29
with respect to the query q as:
P t
r∈P Res(u,q) nr ∗ sim(r, q) In the example in Figure 3, setting a threshold γ = 0.20,
relp (t, u, q) = (5)
nt the system would suggest the tags sport and newspaper.
P t
r∈S Res(q) nr ∗ sim(r, q) 4. EXPERIMENTAL EVALUATION
rels (t, q) = (6)
nt We designed two different experimental sessions to evalu-
where ntr is the number of occurrences of the tag t in the an- ate the performance of the tag recommender. In the first ses-
notation for resource r and nt is the sum of the occurrences sion we performed a comparison between the original scoring
of tag t among all similar resources. function of Lucene and a novel BM25 implementation, while
Finally, the set of Candidate Tags can be defined as: the second was carried out to tune the system parameters.
4.1 Description of the dataset
candT ags(u, q) = candT agsp(u, q) ∪ candT agss (q) (7) We designed the experimental evaluation by exploiting a
where for each tag t the global relevance can be defined as: dataset gathered from Bibsonomy. It contains 263,004 book-
mark posts and 158,924 BibTeX entries submitted by 3,617
different users. For each of the 235,328 different URLs and
rel(t, q) = α ∗ relp (t, q) + (1 − α) ∗ rels (t, q) (8) the 143,050 different BibTeX entries were also provided some
where α (PersonalTagWeight) and (1−α) (SocialTagWeight) textual metadata (such as the title of the resource, the de-
are the weights of the personal and social tags respectively. scription, the abstract and so on).
Figure 3 depicts the procedure performed by the Tag Ex- We evaluated STaR by comparing the real tags (namely,
tractor : in this case we have a set of 4 Social Tags (Newspa- the tags a user adopts to annotate an unseen resource) with
per, Online, Football and Inter) and 3 Personal Tags (Sport, the suggested ones. The accuracy was finally computed us-
Newspaper and Tuttosport). These sets are then merged, ing classical IR metrics, such as Precision, Recall and F1-
building the set of Candidate Tags. This set contains 6 tags Measure. Precision (Pr) is defined as the number of relevant
since the tag newspaper appears both in social and personal recommended tags divided by the number of recommended
tags. The system associates a score to each tag that indi- tags. Recall (Re) is defined as the number of relevant rec-
cates its effectiveness for the target resource. Besides, the ommended tags divided by the total number of relevant tags
scores for the Candidate Tags are weighted again according available. The F1-measure is computed by the following for-
to SocialTagWeight (α) and PersonalTagWeight (1 − α) val- mula:
ues (in the example, 0.3 and 0.7 respectively), in order to
boost the tags already used by the user in the final tag rank. (2 ∗ P r ∗ Re)
Indeed, we can point out that the social tag ‘football’ gets F1 = (9)
P r + Re
the same score of the personal tag ‘tuttosport’, although its
original weight was twice. 4.2 Experimental Session 1
3.4 Tag Recommendation Firstly, we tried to evaluate the predictive accuracy of
STaR comparing difference scoring function (namely, the
Finally, the last step of the recommendation process is
Lucene original one and the aforementioned BM25 imple-
performed by the Filter. It removes from the list of can-
mentation). We performed the same steps previously de-
didate tags the ones not matching specific conditions, such
scribed, retrieving the most similar items using the two men-
as a threshold for the relevance score computed by the Tag
tioned similarity functions and comparing the tags suggested
Extractor. Obviously, the value for the threshold and the
by the system in both cases. Results are presented in Table
maximum number of tags to be recommend is strictly de-
1.
pendent from the training data.
In general, there is an improvement by adopting BM25
Formally, given a user u ∈ U , a query q and a thresh-
with respect to the Lucene original similarity function. We
old value γ, the goal of the filtering component is to build
can note that BM25 improved the both the recall (+ 6,95%
rec(u, q) defined as follows:
for bookmarks, +1,46% for BibTeXs entries) and the F1
measure (+ 2,86% for bookmarks, +0,17% for BibTeXs en-
rec(u, q) = {t ∈ candT ags(u, q)|rel(t, q) > γ} tries).
Figure 3: Description of the process performed by the Tag Extractor
4.3 Experimental Session 2 each resource in the dataset there are many textual fields,
Next we designed a second experimental evaluation in or- such as title, abstract, description, extended description, etc.
der to compare the predictive accuracy of STaR with differ- In this case we used as query the title of the webpage (for
ent combinations of system parameters. Namely: bookmarks) and the title of the publication (for BibTeX en-
tries).
• the maximum number of similar documents retrieved The last parameter we need to tune is the threshold to
by Lucene; deem a tag as relevant (γ).We performed some tests sug-
gesting both 4 and 5 tags and we decided to recommend
• the value of α for the PersonalTagWeight and Social- only 4 tags since the fifth was usually noisy. We also fixed
TagWeight parameters; the threshold value between 0.20 and 0.25.
• the threshold γ to establish whether a tag is relevant; In order to carry out this experimental session we used the
aforementioned dataset both as training and test set. We ex-
• which fields of the target resource use to compose the ecuted the test over 50, 000 bookmarks and 50, 000 BibTeXs.
query; For each resource randomly chosen from the dataset and for
each combination of parameters, we executed the following
• the best scoring function between Lucene standard one steps:
and Okapi BM25.
• query preparation;
First, tuning the number of similar documents to retrieve
from the PersonalIndex and SocialIndex is very important, • Lucene retrieval function invocation;
since a value too high can introduce noise in the retrieval
process, while a value too low can exclude documents con- • building of the set of Candidate Tags;
taining relevant tags. By analyzing the results returned by
• comparing the recommended tags with the real tags
some test queries, we decided to set this value between 5
associated by the user;
and 10, depending on the training data.
Next, we tried to estimate the values for PersonalTag- • computing of Precision, Recall, and F1-measure.
Weight (PTW) and the SocialTagWeight (STW). An higher
weight for the Personal Tags means that in the recommenda- Results are presented in Table 2 and Table 3.
tion process the systems will weigh more the tags previously
used by the target user, while an higher value for the So- Analyzing the results (see Figure ??), it emerges that the
cial Tags will give more importance to the tags used by the approach we called user-based outperformed the other ones.
community (namely, the whole folksonomy) on the target In this configuration we set PTW to 1.0 and STW to 0, so
resource. These parameters are biased by the user practice: we suggest only the tags already used by the user in tagging
if tags often used by the user are very different from those similar resources. No query was submitted against the So-
used from the community, the PTW should be higher than cialIndex. The first remark we can make is that each user
STW. We performed an empirical study since it is difficult to has her own mental model and her own vocabulary: she usu-
define the user behavior at run time. We tested the system ally prefers to tag resources with labels she already used.
setting the parameters with several combinations of values: Instead, getting tags from the SocialIndex only (as proved
i) PTW = 0.7 STW = 0.3; by the results of the community-based approach) often in-
ii) PTW = 0.5 STW = 0.5; troduces some noise in the recommendation process. The
iii) PTW = 0.3 STW = 0.7. hybrid approaches outperformed the community-based one,
Another parameter that can influence the system perfor- but their predictive accuracy is still worse when compared
mance is the set of fields to use to compose the query. For with the user-based approach. Finally, all the approaches
not similar items, maybe exploiting structured data or do-
Table 2: Predictive accuracy of STaR over 50, 000 main ontologies. Furthermore, since tags usually suffer of
bookmarks typical Information Retrieval problem (namely, polysemy,
Approach STW PTW Pr Re F1
synonymy, etc.) we will try to establish if the integration
of Word Sense Disambiguation tools or a semantic repre-
Comm.-based 1.0 0.0 23.96 24.60 24.28
sentation of documents could improve the performance of
User-based 0.0 1.0 32.12 28.72 30.33
recommender. Another issue to analyze is the application
of our methodology in different domains such as multimedia
Hybrid 0.7 0.3 24.96 26.30 25.61
environment. In this field discovering similarity among items
Hybrid 0.5 0.5 24.10 25.16 24.62
just on the ground of textual content could be not sufficient.
Hybrid 0.3 0.7 23.85 25.12 25.08
Finally, we will perform also some studies in the area of
Baseline - - 35.58 10.42 16.11
tag-based recommendation, investigating the integration of
tag recommenders for recommendations tasks, since reach-
ing more quickly the tag convergence could help to build
better folksonomies and to produce more accurate recom-
Table 3: Predictive accuracy of STaR over 50, 000 mendations.
BibTeXs
Approach STW PTW Pr Re F1
6. REFERENCES
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Content-based Recommender System Integrating Tags
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Hybrid 0.5 0.5 32.36 37.55 34.76 K. Ng, and J. Delgado, editors, Proceedings of the 4th
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