=Paper=
{{Paper
|id=None
|storemode=property
|title=Tag and Neighbour Based Recommender System for Medical Events
|pdfUrl=https://ceur-ws.org/Vol-572/paper2.pdf
|volume=Vol-572
}}
==Tag and Neighbour Based Recommender System for Medical Events==
https://ceur-ws.org/Vol-572/paper2.pdf
Tag and Neighbour Based Recommender System
for Medical Events
Karunakar Reddy Bayyapu and Peter Dolog
IWIS — Intelligent Web and Information Systems,
Aalborg University, Computer Science Department
Selma Lagerlöfs Vej 300 DK-9220 Aalborg, Denmark
E-mail: {kreddy, dolog}@cs.aau.dk
Abstract. This paper presents an extension of a multifactor recommen-
dation approach based on user tagging with term neighbours. Neighbours
of words in tag vectors and documents provide for hitting larger set of
documents and not only those matching with direct tag vectors or con-
tent of the documents. Tag popularity, tag representativeness and tag
similarity are applied similarly as in the original approach but also to
neighbours. By doing so, we treat the documents which have been added
to the result set by considering word neighbours in the same way as the
others. This provides an advantage in the situations where the quality of
tags is lower. We discuss the approach on the examples from the existing
Medworm system to indicate the usefulness of the approach.
1 Introduction
Search systems typically return a ranked list of web pages on different aspects
of the same topic in the returned list in a response to a users request. Recently,
social activities such as tagging have emerged mostly to help people to organize
resources of their personal interest on the web. The tagging information has been
applied to help information retrieval and recommender systems. Although some
successful applications have been developed (see, for instance,[4]), implementing
and extending a hybrid tag-based recommender system with personalization for
social bookmarking systems is still a challenge. Many applications would benefit
from tag based analysis, which is sufficiently general to be useful in a wide range
of applications, is already performed.
Tags in Medical bookmarking systems such as M edworm are usually assigned
to organize and share resources on the Web. Tag clouds are weighted lists of tags.
The relative importance of a tag is visualized with bigger font size, bolder letters,
and is measured as a count of the popularity of the tag i.e. how many times users
have used it to describe a resource. Tags are generally submitted by any user
in bookmarking services such as for example http : //medworm.com. The data
source tags provide useful information with sufficient background and context,
even though the set of tags are quite limited to provide an accurate degree of
relatedness between tags and neighbours.
14 MEDEX 2010 Proceedings
Furthermore, the tags themselves as they appear in theM edworn system, for
example, represent multiple domains. Therefore, it is not directly applicable to
consider only tag measures. In our approach, we focus our efforts on neighbour
objects of the tags for search and retrieval systems. Our approach combines basic
similarity calculus with external factors such as a tag popularity, tag represen-
tativeness and closest neighbours semantic similarity, document score, semantic
similarity of tags as described in [4]. The proposed contribution of this paper is:
– The extended hybrid tag based recommender system which bases the com-
putation on a user query,
– Finding the closest neighbour vector of the tag from medical data source.
The rest of the paper is structured as follows. Section 2 discusses the prob-
lem and proposed solution on an example. Section 3 defines our approach to
multi-factor recommendation with word neighbours. Section 4 discusses related
work and positions our work in this context. Section 5 discusses experimental
evaluations and outcome results. Section 6 explains analysis of experiment and
conclusions .Section 7 discusses future work.
2 Working example and Motivating scenario
Let’s consider the following scenario. If the user submits a query to the
system, the system evaluates which documents are relevant to the query and
returns a rank ordered list of documents to the users. Normally, the system
considers content of the documents or collaborative user activities as factors
to judge the relevance. Recently, tag-based approaches have been coined in the
literature as well but only in the general situations. The domain specific searches
by experts usually do not hit the most relevant documents in the first top n
results. For example, the medical M edworm system gives almost 14470 records
just based on swine flu tag. However, the system does not know what exactly
user is looking for, or user doesn’t know the proper words to describe what it is
that he wants. Then the returned results are often unsatisfactory.
However, we could resolve this problem by proposed extension of tag and
neighbour based recommender systems for medical events. This approach searches
for the most trusted information. The traditional approach based on simple tag
based recommendation factors are not efficient enough for domain specific sys-
tems such as M edworm due to lower relevance of user generated tags used.
Therefore, we also apply neighbours to consider wider set of documents.
Figure 1 shows which kind of information M edworm can offer to improve
the search to find information with respect to a certain aspect of a document.
One just needs to refer to its associated tags and predicted neighbours in the
corresponding documents. There are two columns depicted in the figure which
represent the positions of neighbours in the text or the tag vector. By consid-
ering one side or both sides of the neighbourhood, we can target wider space of
documents than with original query. This allows for expansion of the document
set hit by the query. Therefore, we ensure that the user will not miss important
MEDEX 2010 Proceedings 15
medical events documented in a document or a blog even when it does not match
exactly the query. By applying personalization factors as in original query to ex-
tracted neighbours, we achieve similar ranking and therefore provide a means to
access the most relevant documents.
Fig. 1. Relation between predicted tags and their neighbours of user?s interest
3 Recommender System
3.1 Concept
The main concept is to achieve recommender system in medical events. The
proposed recommender system combines associated neighbours with different
aspects of similarity and tags. Consequently, a hybrid recommender system ob-
tained integrate many independent recommendations by applying tag popularity,
tag representativeness, tag similarity and neighbours. It exploits neighbours that
are dynamically re-calculated according to the effectiveness of the recommenda-
tion. The system also uses the semantic similarity between the neighbours to
calculate neighbours set. These results influence the final recommendation list
to re-order the rank. The process of the calculation looks as follows. First, a
users click on a tag or he submits a query. Then, the recommendation algorithm
is applied to produce a set of recommended resources. Second, this set is then
sorted by taking the user interest and tag neighbours into account and re-ranks
the results accordingly.
3.2 Multi-Factor Recommendation with Word Neighbours
The tag and neighbour based recommender approach based on [4] is calcu-
lated as:
16 MEDEX 2010 Proceedings
The extended hybrid similarity score (HS),
HS(Di ,Dii ) = [(DsDi × DsDii ) × (T S(Di ,Dii ) )] × N S(Di ,Dii ) ,
where Di and Dii are a particular documents from a set of documents D. Ds
is the document score, T S is a function for measuring the tag similarity and N S
is the closest neighbour vector space. We define document score as [4]:
Pn Pn
Ds = i=1 P opularity(T agi ) × i=1 Representativeness(T agi ),
where n is the total number of existing tags in the repository and for the
definitions see in [4]. Informally, each one of the factors in the above formulas is
calculated as follows:
Tag Popularity. The tag popularity is calculated as a count of occurrences of
one tag per total of resources available [4]. We rely on the fact that the most
popular tags are like anchors to the most confident resources. As a consequence,
it decreases the chance of dissatisfaction by the receivers of the recommendations.
Tag Representativeness. [4] It measures how much a tag can represent a doc-
ument it belongs to. It is believed that those tags which most appear in the
document can better represent it. The tag representativeness is measured by the
term frequency.
Tag Similarity (TS). It combines the classical cosine similarity (CosSim) from
user query and information retrieval field with a semantic similarity (SemSim)
which is defined in [4].
Cosine similarity (CosSim). The cosine similarity in our approach is a measure
between, a user query Q transformed into a tag vectore and a set of tags or
words (represented as a vector) of particular web page (W̄ ). Each word, w(ti),
in each dimension corresponds to the importance of a particular tag ti.
The Cosine similarity (Q, wi) is calculated for every tag word resource wi ∈
W̄ . As an output, this stage of the algorithm will produce a subset of resources
W 0 , that have some similarity to the query tag and similarity scores for each
[15].
Let us assume that the user interacts with the system by selecting a query
tag and expects to receive resource recommendations. Therefore, a query is a
unit vector consisting of a single tag, and the equation is (adapted from [12]):
W̄(QD ,WD )
CosSim(QDi ,wDii ) = qP i ii
t∈T W̄(QDi ,WDii )
where T is the set of tags, the similarity of the selected tag to each resource
and recommends the top n., W̄ is the set of words of that particular visited web
page, Di and Dii are a particular documents from a set of web pages/documents.
MEDEX 2010 Proceedings 17
Semantic Similarity (SemSim). The semantic relation between two tags is de-
fined as follows:
SemSim(s, t) = M DSim(s, t) × OntoSim(s, t), ∀s, t ∈ T
Where s and t are particular tags of set of tags T . MD Sim (s,t) is the
Medical Dictionary similarity score and OntoSim (s,t) is the similarity score
achieved from ontologies.
Neighbour Semantic Similarity (NS). Calculates closest neighbour influence for
personalized recommendation by vector space models [13]. In order to find the
nearest neighbours of the tag word, it must measure the similarity of the tag
words [20], and select several words that have the highest similarity as the nearest
neighbours of the tag word. We adopt cosine similarity algorithm to measure the
similarity between word w(ti) and w(tj). If the user does not rate the words, we
can assume the rating is zero. Assuming the rating of the n-dimensional word
space of word w(ti) and w(tj) is respectively vector w(ti)¯ and w(tj).¯
The similarity between word w(ti) and w(tj) is sim(w(ti), w(tj))
¯ ¯
¯ w(tj)
sim(w(ti), w(tj)) = cos w(ti), ¯ = w(¯ti)∗w(tj)¯
kw(ti)k∗kw(tj)k
In this view, the solution to be addressed includes how to represent the tags
and their closest neighbours and how to use it to influence the activation of
user preferences. The approach is to predicted neighbours, the current visited
context is represented as (is approximated by) a set of words concepts from the
domain ontology. Ultimately, the perceived effect of neighbours extended hybrid
approach is that user interests that are in focus for a current context, and those
that are in the semantic scope of the ongoing user activity are considered for
personalization [3].
4 Related Work
Tags have been recently studied in the context of recommender systems due
to various reasons. Tags are signals or labels that particular resource was in-
teresting for a user, and he bookmarked it as well as tagged it with a specific
tag relevant for a particular situation a user was encountered in. Recommenda-
tions of relevant events should be based on the sufficient occurrences for similar
signals expressed by tags. Therefore, different similarity measures need to be
studied in this context for effectiveness and efficiency. [10] argues for a solution
where tagging from social bookmarking provides a context for recommender sys-
tems in terms of context clues from tags as well as connectivity among users to
improve the collaborative recommender system. [11] constructed a web recom-
mender based on large amount of public bookmark data on Social Bookmarking
systems. For means of personalization, [11] utilizes folksonomy tags to classify
web pages and to express user’s preferences. By clustering folksonomy tags, they
18 MEDEX 2010 Proceedings
can adjust the abstraction level of user’s preferences to the appropriate level.
[11] experiment did not measure the efficiency of the recommendations in terms
of user satisfaction what could give us a parameter for comparison. [17] extends
a content based recommender system by deriving current and general personal
interests of users from different tags according to different time intervals. How-
ever, the similarity of the tags is given by two Na?ve Bayes classifiers trained over
different timeframes: one classifier predicts the user’s current interest, whereas
the other classifier predicts the user’s general interest in a bookmark. The two
classifiers are trained with a subset of the bookmarks created by a user. The
tags of each bookmark, converted into a ”bag of words”, are used as training
features. The bookmarks are recommended in the case of both two classifiers
predicting a bookmark as interesting. The effectiveness of the recommendations,
however, is totally dependent on the quality of the subset of bookmarks used for
training the classifiers.
[19] proposes a collaborative filtering approach TBCF (Tag-based Collabora-
tive Filtering) based on the semantic distance among tags assigned by different
users to improve the effectiveness of neighbour selection. That is, two users
could be considered similar not only if they rated the items similarly, but also
if they have similar understanding over these items. To calculate the semantic
similarity, the WordNet dictionary is being accessed to find the shortest path
connecting a tag and its synonym in the graph synsets. The semantic distance
based calculation, which might be difficult depending on the context of users.
Special vocabularies hardly are found in general purpose dictionaries such as
WordNet. Furthermore, the WordNet lacks much data useful to support proper
name disambiguation, and it is not collaboratively edited [8]. [7] develops a page
rank based algorithm for recommendations of resources based on preference vec-
tors in folksonomy systems. [5] shows the benefits of using tag based profiles for
personalized recommendations of music on Last.fm. The purpose of tags varies
as well as tagging itself may be influenced by different factors. For example, [14]
studies a model for tagging evolution based on the community influence and
personal tendency. It shows how 4 different options to display tags affect user’s
tagging behavior. [1] studies how the tags are used for search purposes. It con-
firms that the tags can represent a different purpose such as topic, self reference,
and so on and that the distribution of usage between the purposes varies across
the domains. It compares the purposes with another literature (such as [6, 18,
14]) where these are called differently.
Other works such as [16] and [9] coined the term emergent semantics as the
semantics which emerge in communities as social agreement on tag’s meaning
that the semantics is derived from its frequent use instead of the contract given
by ontologies from ontology engineering point of view. However, the approaches
based on emergent semantics are characterized by the power law which gives
a long tail of the tags of which semantics have not emerged yet. Therefore, [2]
looks at grounding of the tag relatedness with a help of WordNet.
MEDEX 2010 Proceedings 19
5 Evaluation and Results
We have conducted an experiment to preliminary assess the performance of
the recommender approach proposed in this paper. The nature of the experiment
was based on a simulation a mix of scenarios regarding the amount of pages, tags
and their neighbours. The proposed scenarios were created aiming at simulating
realistic usage of M edworm. The variables addressed by each scenario are:
– Amount of Pages: each page has a set of tags that are compared for process-
ing the recommendations. Therefore, the more pages exist then more time
will be spent to calculate the similarity between the pages.
– Amount of Tags: the similarity of the pages is given by their tags. The whole
set of tags of each page must be compared to verify which ones are similar.
– Set of neighbours: set of neighbours are depending on similarity of the tags.
The whole set of neighbours of each page must be compared with semantic
meaning of the tag.
These variables were chosen because we are using them for calculating the
recommendations. This process is time consuming and invariably affects the
system performance. However, it does not mean that other factors such as page
size should not be considered[4].
We found that the choice of tf ∗ idf played an important role to find tag rep-
resentativeness. In our evaluation, tf idf have identical trends, but tf idf always
provides superior results, so we have reported only results found based on those
weights. We were able to extract tag neighbours. Some samples were taken from
each dataset of neighbours to find neighbours semantic similarity using cosine
similarity. The validation was performed to measure the improvement in rec-
ommendation. We used MedicineNet1 online free medical dictionary to measure
semantic similarity.
Let’s assume that given semantic similarity is ϕ(s), where S is the semantic
similarity. We also have to define cosine similarity between the user’s query and
tags ti, tj, tk, tl, etc. These tags could be in vector node of current webpage:
W̄ = {. . . , ti, . . . , tj, . . . , tk, . . . , tl . . . . . . }, where ϕ(s) ⊆ w̄ and ϕ(s) ∩ w̄ = ∅
The neighbour similarity depends on the similarity of the tag words. So,
neighbour semantic similarity (NS) is subset of cosine similarity. NS can be
computed as follows:
ϕ̄(N S) = {. . . , ti − 1, ti + 1, . . . , tj − 1, tj + 1, . . . , tk − 1, tk + 1, . . . , tl − 1, tl +
1, . . . . . . }, ϕ̄(N S) ⊆ w̄ and ϕ̄(N S) ∼ ϕ(s).
In order to test the effectiveness of the algorithm, we compute the factors
with a collection of documents from M edworm data source about 90 pages.
The documents were encoded with xml format, so we decided to make 466 tags
manually. Content of the pages and some tags were extracted from web sites
on the Internet. Similarly, we utilized manually generated neighbours to assign
tags to M edworm pages tagged by a particular user. Due to certain constraints,
1
http://www.medicinenet.com
20 MEDEX 2010 Proceedings
we had to limit the number of user queries. We needed just adequate number
of satisfactorily different pages and sufficiently different assignment of tags to
them. Each test case consists of tag, neighbours, semantic factors and resource.
We consider this resource as the target results, since we know that the user is
interested in it.
Here, we have one user interest query “avian flu pandemic”. A simple way
to start out is by eliminating documents that do not contain all three tags
“avian”, “flu”, “pandemic”, but in general it hits many documents. Our algo-
rithm distinguished relevant and irrelevant documents and tags like “avian”,
“flu”, “pandemic” that occur rarely and good keywords. After performing a rec-
ommendation using both the tag and neighbours, the rank of the target resource
in the recommendation set was recorded and shown in table1.
Table 1. First five documents with highest HS returned by our Hybrid Recommenda-
tion approach for a query= avian flu pandemic. The top document entitled Birds in
the news, is intuitively relevant to the query
ReturnPos Document# Document score(DS) Recommedation Score(HS)
1 34 19.12 14.531
2 12 14.06 8.857
3 56 12.03 4.879
4 8 10.05 3.601
5 44 10.79 2.421
As seen at Table 1 our extended hybrid based algorithm accepts a user inter-
est, a set of neighbours, and a selected tag. The recommendation and document
scores are from the interval between 0 and 20. As we can see, the top recommen-
dation score is 14.53 (72.65% accuracy). It means that the particular document
is the most relevant to users query. Result positions 2, 3, 4 are also relevant
to user query but not most relevant when comparing to position 1. Position 5
document got mixed results and it is partially related to the query. This result
was not considered excellent but satisfactory since our recommendations relied
basically on syntax similarity of the tags.
6 Discussions and Conclusions
Categorized M edworm is the application of medical RSS feeds 2 to better
target the delivery of health care, facilitate the discovery of new products, and
helps to determine a person’s predisposition to a particular disease or condi-
tion through web. In this recommender systems, the extended hybrid algorithm
2
http://www.medworm.com/rss/aboutmedworm.php
MEDEX 2010 Proceedings 21
can perform tasks such as discovering documents (much like the web robots),
ranking documents, filtering them, and automatically routing useful and inter-
esting information to users and it has learning and adaptation capabilities. In
fact, the extended hybrid algorithm perfectly suits information discovery and
retrieval in the web. For example, information discovery and ranking can be
handled by document score which depends on the tag popularity and tag rep-
resentativeness. Another tag similarity can specialize in indexing, yet another
like neighbours similarity can implement an information retrieval, and so forth.
Applying these techniques to the web pages/documents, retrieved by a search
tool could substantially weed out unrelated documents and improve the ranking
quality of the remaining page/documents [15].
As per our experiment, the user has tagged several medical object web re-
sources with the tag “flu”. If a user selects that tag, the system should recom-
mend resources concerning the number of records about the “flu”. Certainly in
addition, another “flu” related documents may have been tagged with alterna-
tive tags: disease, swine, H1N1, avian, bird flu, influenza, flu attacks, respiratory
problems, pandemic, symptoms, lung infection, etc. These resources may have
been tagged with “flu” and may not have been “flu” but they should still be
made available to a user. The recommendation strategies must also be adapted
to deal with the neighbours. Typically, recommender systems have dealt with
two dimensions: users query and object neighbours semantic similarity. Consider
again the “flu” related topics. After selecting “flu” the system may recommend
resources only related by semantic similarities of neighbours. User may notice
as of his query “avian flu pandemic”, the “avian”, “flu”, and “pandemic” are
related tags. These are generated by the closest neighbour semantic objects.
Finally, user may notice resources in Medworm’s profile dealing with the
medical neighbour objects, and view one of those resources as his priority is
most trusted information in the top position.
7 Future Work
The evaluation of the system provided some supplementary conclusions,
namely, a recommendation performed with association neighbours appeared to
be the most useful but only positive influence neighbours. Future work will focus
on positive and negative neighbours and tag similarity i.e. sentiment analysis. It
would benefit the system to utilize such opinions and to lower the score of bad
results, even if other strategies show them as recommendable.
Acknowledgments
This work is partially supported by the European Union under the IST
project M-Eco (http://www.meco-project.eu).
22 MEDEX 2010 Proceedings
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