=Paper=
{{Paper
|id=Vol-371/paper-2
|storemode=property
|title=Towards using contextual information to learn trust metric in social networks: A Proposal
|pdfUrl=https://ceur-ws.org/Vol-371/CAT08_S.pdf
|volume=Vol-371
}}
==Towards using contextual information to learn trust metric in social networks: A Proposal==
https://ceur-ws.org/Vol-371/CAT08_S.pdf
Towards Context-Enriched Trust Prediction:
A Proposal
Marcin Sydow
Polish-Japanese Institute of Information Technology,
Web Mining Lab,
Koszykowa 86, 02-008, Warszawa, Poland??
msyd@pjwstk.edu.pl
Abstract. This short paper describes an early stage of research aiming
at improving trust prediction in social networks.
It builds on recent findings on observed correlation between trust and
user similarity. A machine-learning approach to the trust prediction prob-
lem with use of contextual information is proposed and experimental
work envisaged.
An overview of the existing results is presented and it indicates that the
proposed approach constitutes a novel combination of ideas and, as such,
has a potential to contribute to the previous research in the area.
Key words: trust metrics, context, experimentation, social networks
1 Introduction
There is an explosion of interest in on-line communities where users’ personal
preferences can be explicitly expressed. Trust management plays an important
role in such systems since on-line users base their decisions on information ex-
pressed by other users. However, the amount of information contained in social
networks to be analysed by the users grows exponentially, so that there is a
strong need for developing and improving automatic techniques that support
users in making their on-line activities.
An example of such automatic support is a recommender system like epinions1
or FilmTrust2 . The system, given the preferences of the users and trust values
expressed among them, is capable of automatically recommending some items
to particular users. In this way, the information about trust among the users
significantly improves the decision-support process.
The mechanism mentioned above can work quite well given that the trust
values are expressed by the users. However, since on-line communities grow con-
stantly, significant part of the users are newcomers to the system so that they
did not specify their trust to other users, yet.
??
The work is supported by the Polish Ministry of Science grant N N516 4307 33.
1
http://www.epninions.com
2
http://trust.mindswap.org/FilmTrust/
Due to this problem, we study the issue which is somehow reverse to the
recommendation problem mentioned earlier. Namely, we aim at studying the
problem of how to better predict an unknown trust between a pair of users
given some additional information available in the system which we call the
contextual information.
The term contextual information in social networks can be interpreted in
many ways. In the most general meaning, the context of trust in a social network
can be understood as all the information available in the social network except
the trust information itself.
In our research, however, we plan to initially narrow the definition of the
contextual information to the recommendations (ratings) given by the users.
Thus, our notion of the context-aware trust might differ from those considered
elsewhere, e.g. in [12] or [10]. This limited understanding of the trust context
definition is partially due to the fact that quite often only the recommendation
data, except the trust data itself, is available in publicly accessible real datasets
concerning trust-related research. In particular, we plan to build user profiles
based on their recommendations, and subsequently treat such profiles as a special
kind of contextual information in the task of trust prediction.
An example of a dataset, with both: trust and recommendation data avail-
able, is the epinion dataset [1]. We plan to start the experimentation with this
particular dataset.
In future, if the results obtained for such a limited definition of the context
of trust are encouraging and we have access to datasets with richer information,
we plan to treat the context of trust in a broader meaning.
2 Motivation and Related Work
A recent study by Ziegler et al. [15] experimentally proves, that there exists a sig-
nificant correlation between the trust expressed by the users and their similarity
based on the recommendations they made in the system.
Our proposal of research builds on that finding and considers going a step
further. Namely, using the recommendations made by the users as the contextual
information we aim at predicting potentially unknown trust between users. The
main difference is that in the cited paper merely the correlation between the
trust and the recommendation-based context is measured, while in contrast, we
plan to measure how much the context helps in a full machine-learning approach
to predicting the unknown trust value.
More precisely, our research aims at experimentally comparing the predictive
value of the three groups of attributes in the machine-learning task of the trust
prediction in social networks. The three groups of attributes are:
1. the attributes based solely on the pure topology of the trust network
2. the attributes based on the contextual information. As was explained, we
plan to begin with taking the recommendation-based information to build
such attributes
3. the combined trust-based and contextual-based attributes
One of the main goals of the proposed research is to experimentally check our
hypothesis which is as follows: a trust-predictor build on the third, contextually-
enriched group of attributes, significantly outperforms the predictors build on
pure-trust and pure-contextual attributes, separately, which seems to be suggested
by the results reported in [15]
A good general introduction to the trust management issues is given in [11].
Trust and distrust propagation models are proposed in [4] and further enhanced
in [13] in the special context of Web spam combating.
The problem of pure link-based classification and appropriate link-based fea-
tures are discussed by Liben-Nowell et al. [7] which proposes some measures
for analysing proximity of nodes in a social undirected network representing co-
authorship. Karamon et al. continues this research [5], however in this work the
problem of node classification rather than link classification is discussed.
Correlation between trust and user profile similarity is discussed in [15] and
further studied – by means of survey-based experiments – in [2]. An algorithm
based on Bayesian network for trust inference is discussed in [6].
In the time of writing, the author discovered an unpublished draft paper
concerning the problem of trust-based rating prediction and, inversely, similarity-
based trust prediction by Matsuo et al. [9]. However, the current version of the
draft does not seem to be completed (at the time of writing), for example, some
important formulae seem to need to be syntactically clarified. However, since
there seems to be a remarkable overlap with what is proposed in our research it
seems necessary to address the issue how they relate to each other. In particular,
in its current form, the mentioned draft:
– proposes general topology-based characteristics for trust computation, which,
for example, never propagate further than through two links. In contrast, this
paper proposes features that are especially designed to trust (and distrust)
propagation measurement and are not constrained in such a way
– unfortunately does not address the problem of the extreme rating-data spar-
sity. In practice, in most of the available datasets the ratings data is too
sparse to be directly processed. Our approach is aware of this issue and
proposes how to try to overcome this problem
– does not seem to take into account the distrust concept
It would be very interesting, though, to compare the final results of the
mentioned paper, and the research envisaged here, when they are ready.
The epinions dataset [1] was studied by Massa et al. [8] where also the
interesting notion of “controversial users” was introduced in the context of the
adversarial behaviour of some users.
The context of trust is extended by the user revision history in [14] and by
the user’s provenance in [3].
3 Proposed Experiments
As motivated at the beginning of the section 2, building on some recently pub-
lished results, as explained above, we propose to experimentally test how much
contextual information helps in trust prediction.
We propose the following experimental scheme:
1. Data: take a real dataset concerning trust network enhanced with some kind
of contextual data such as ratings, user profiles, etc. At the beginning we
plan to experiment with the publicly available epinions dataset [1], which
contains both trust network and user recommendations, the latter one will
be used as the contextual information
2. Sampling: from the dataset, take a random sample of user pairs for sub-
sequent trust-prediction task. Due to the sizes of available real datasets,
the epinions dataset in particular, sampling seems to be necessary, since re-
peating the experiment on all pairs of users in the dataset3 seems to be
computationally too expensive.
3. Training-testing split: as a common technique in supervised learning, to avoid
evaluating the trust-prediction results on the same examples on which the
predictor was trained (which is referred to as the over-fitting problem) split
the sample into the training and testing subsets. We plan to tune the train-
ing/testing size ratio experimentally. The splitting should be stratified i.e.
the proportion of the ‘trusting’ and ‘distrusting’ user pairs in sub-samples
should be close to that observed in the whole sample.
4. Pure trust-topology prediction baseline: predict the value of trust (or dis-
trust) between the nodes in the sample, based only on the trust-network
information (without taking into account the actual known trust value on
(u, v)). We propose to apply special trust-propagation techniques [4, 13].
5. Pure contextual-based prediction baseline: Compute contextual features,
based on the additional information available in the dataset, such as user
recommendations, similar to those described in [15, 2] and predict the trust
using only these features
6. Contextually-enriched predictor: combine the trust-based and context-based
features and finally train the combined trust predictor on the same sample
as above.
7. Evaluation: compare the performance of the three prediction schemes above
with use of some standard prediction-performance measures, such as predic-
tion accuracy, the F-measure, etc.
The details about how the planned experiments relate to the previous research
in the area and in which way the proposed experiments are novel are described
in the following section.
3
which is O(n2 ) for the number of users being n
4 Planned Contributions
The experimental work proposed in this paper is expected to contribute to the
existing related research (see Section 2) in the following ways:
– supervised machine-learning approach, to the trust-prediction problem in-
stead of a simple linear correlation measurement [15]. It seems that at least
2 different algorithms should be tested to have more reliable results, e.g. the
Weka4 implementation of the C4.5 decision tree and SVM.
– introduce some features based on especially designed trust-propagation mod-
els [4, 13]. Previously, only some general link-topology characteristics [7, 5]
were used in this context.
– due to the extreme sparsity of the contextual data, when computing the
ratings-based similarity between the users, we propose to first cluster the
rated items with a method which is aware of the bi-partite structure of the
rating graph. Independently, some dimension-reduction techniques such as
SVD can be subsequently considered, if needed, to make the user profiles
more overlapping. If a hierarchical categorisation of the rated items is avail-
able in the datasets, it should be used as well (as in [15]). Similar techniques
concerning clustering the users could be considered. Due to this, the exper-
iments can take into account also the users with little (or no) overlap in the
rating-based profiles, in contrast to the previously reported approaches.
– in case such data is available, introduce a distrust prediction, which is a
mathematically different task to trust prediction (e.g. distrust does not seem
to allow for simple transitive propagation, in contrast to trust)
– when using rating data, take into account the true meaning of the values.
Some previous approaches seem to not distinguish between ‘positive’ and
‘negative’ interpretation of the numerical values assigned to the ratings (and
apply e.g. a dot-product similarity measure which is wrong in this context)
– on later stages of the research, if the textual ratings are available in the
dataset, consider textual similarity of the textual reviews to compute more
user similarity based features
To the best knowledge of the author, the combination of the propositions
listed above is novel and can make the results of the proposed experimental
research a potentially valuable contribution to the current research in the area.
5 Conclusions
Based on recently reported experimental results concerning observed significant
correlation between user similarity and trust in social networks, a plan of further
exploration of this direction is proposed here.
We propose a machine-learning approach to build a trust predictor and ex-
perimentally measure how much the contextual information can improve the
4
http://www.cs.waikato.ac.nz/ml/weka
trust prediction accuracy compared to some trust prediction models which are
based solely on pure topology of trust networks. We plan to start experimenta-
tion on the epinions dataset [1] and initially limit the contextual information to
user recommendations, since such data is currently at our disposal.
References
1. http://www.trustlet.org.
2. Jennifer Golbeck. Trust and nuanced profile similarity in online social networks.
Technical report.
3. Jennifer Golbeck. Combining provenance with trust in social networks for semantic
web content filtering. In IPAW, pages 101–108, 2006.
4. R. Guha, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. Propagation of
trust and distrust. In WWW ’04: Proceedings of the 13th international conference
on World Wide Web, pages 403–412, New York, NY, USA, 2004. ACM.
5. J. Karamon, Y. Matsuo, H. Yamamoto, and M. Ishizuka. Generating social network
features for link-based classification. In PKDD, pages 127–139, 2007.
6. U. Kuter and J. Golbeck. Sunny: A new algorithm for trust inference in social
networks using probabilistic confidence models. In AAAI, pages 1377–1382, 2007.
7. David Liben-Nowell and Jon Kleinberg. The link prediction problem for social
networks. In Proceedings of CIKM ’03, pages 556–559, NY, USA, 2003. ACM.
8. P. Massa and P. Avesani. Controversial users demand local trust metrics: An
experimental study on epinions.com community. In AAAI, pages 121–126, 2005.
9. Yutaka Matsuo and Hikaru Yamamoto. Measuring bidirectional effects on trust
and rating on online social networks. Unpublished.
10. R. Neisse, M. Wegdam, P. Dockhorn Costa, and M. J. van Sinderen. Context-aware
management domains. In B. Hulsebosch, G. Lenzini, and M. Wegdam, editors, Pro-
ceedings of Context Awareness and Trust 2007 (CAT07), First International Work-
shop on Combining Context with Trust, Security and Privacy, Moncton, Canada,
volume 269 of CEUR Workshop Proceedings, pages 42–47, Nederlands, July 2007.
11. Sini Ruohomaa and Lea Kutvonen. Trust management survey. In Proceedings of
the iTrust 3rd International Conference on Trust Management, 23–26, May, 2005,
Rocquencourt, France, pages 77–92. Springer-Verlag, LNCS 3477/2005, May 2005.
12. Sini Ruohomaa and Lea Kutvonen. Making multi-dimensional trust decisions on
inter-enterprise collaborations. In Proceedings of the Third International Con-
ference on Availability, Security and Reliability (ARES 2008), pages 873–880,
Barcelona, Spain, March 2008. IEEE Computer Society.
13. Baoning Wu, Vinay Goel, and Brian D. Davison. Propagating trust and distrust
to demote web spam. In Workshop on Models of Trust for the Web, Edinburgh,
Scotland, May 2006.
14. Honglei Zeng, Maher Alhossaini, Li Ding, Richard Fikes, and Deborah L. McGuin-
ness. Computing Trust from Revision History. In Proceedings of the 2006 Inter-
national Conference on Privacy, Security and Trust, October 2006.
15. Cai-Nicolas Ziegler and Jennifer Golbeck. Investigating interactions of trust and
interest similarity. Decis. Support Syst., 43(2):460–475, 2007.