The document presents a study on using deep neural networks to match online social networking profiles that belong to the same individual. It describes extracting features from profiles, including domain-specific and text-based features. A deep neural network model with multiple fully-connected layers is proposed and shown to achieve high precision and recall on a large dataset, outperforming other supervised and unsupervised baseline methods. The study demonstrates applying deep learning techniques to the task of linking profiles from different social networks that refer to the same person.

1. Deep neural networks for matching
online social networking profiles
Vicentiu-Marian Ciorbaru & Traian Rebedea
University Politehnica of Bucharest, Romania
ICCCI 2017
Nicosia, Sep 27th

2. Outline
› Introduction
› Related work
– Personal web pages deduplication
– Social networking profiles matching
› Dataset
› Proposed approach
– Unsupervised vs Supervised
– Extracted features
– Deep neural network for profile matching
› Results
› Conclusions
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Deep neural networks for matching
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3. Introduction
› Online people search is a significant part of web search
(Artiles et al., 2010)
– 11-17% of queries include a person name
– ~4% contain only a person name
› Name ambiguity makes people search a complex problem to
solve efficiently
– Huge overlap in person names worldwide
– The most popular 90,000 full names (first and last name) worldwide are
shared by 100M+ individuals
› An important aspect in people search is to find most/all online
sources of information (e.g. web pages) related to the same
person
– Recent shift from general web pages to specific ones, like social
networking sites and other professional communities
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Deep neural networks for matching
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4. Deep neural networks for matching
online social networking profiles
Introduction
› Our problem: given a set of web pages extracted from online social
networks, determine the profiles which relate to the same individual
– Profile matching (or deduplication)
– Generates a (more) complete online identity for an individual
– Only uses public online information, however adding up all this information
about a person can cause privacy concerns
Deep neural networks for matching
online social networking prfioles
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5. Related work
› Two main directions
– Personal web pages deduplication
– Matching social networking profiles
› First problem is more generic and complex, as one also
needs to extract personal information (e.g. name,
occupation, etc.) from a wide range of different structured
web pages
› Entity deduplication, in general, is a very complex field of
study in Databases, Natural Language Processing (NLP),
and Information Retrieval
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Deep neural networks for matching
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6. Related work
› Web People Search (WePS) datasets and competitions (Artiles et al., 2009 & 2010)
› Given all web pages returned by a generic search engine for a popular name, group pages
such that each group corresponds to one specic person
› Most solutions employ clustering of the web pages using features extracted from pages
such as Wikipedia concepts, Named Entity Recognition (NER), bag of words (BoW), and
hyperlinks and different similarity measures
› A pairwise approach for solving this problem was also proposed
– Compute the probability that two pages refer to the same person
– Cluster pages by joining pairs that have a high probability to represent the same person
› WePS proposed B-cubed precision and recall for assessing performance
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Nicosia, Sep 27th
Deep neural networks for matching
online social networking profiles

7. Related work
› More recent research focused on linking social networking
profiles belonging to the same individual
› Zhang et al. (2015) proposed a binary classifier using a
probabilistic graphical model (factor graph)
› Features computed using BoW and TF-IDF for the text in
each profile, but also its social status (position of node in
network) and connections
› Our solutions only uses textual features, since the dataset
does not contain connections (e.g. friends or followers)
– These additional features, or other like avatar/profile image, would
only improve the results
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Deep neural networks for matching
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8. Dataset
› Snapshot of multiple social networking profiles collected from
15 different online social networks and community websites
– Academia, Code-Project, Facebook, Github, Google+, Instagram, Lanyrd, Linkedin, Mashable, Medium,
Moz, Quora, Slideshare, Twitter, and Vimeo
› For each profile, we extracted some/all of the following
information: username, name (full name or distinct first and last
names), gender, bio (short description), interests, publications,
jobs, etc.
› The average number of social profiles per individual is 2.04
and the maximum is 10
› Most profile pages feature a brief description (bio) of the owner
› Profiles do not contain connections, nor posts written by the
owner
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Deep neural networks for matching
online social networking profiles

9. Dataset
› Ground truth obtained from the website about.me
– Complete online information for professionals
– Contains links to several social networking profiles
of the same person, added manually by each user
› Dataset contains information from over 200,000
about.me accounts
› Total number of extracted social networking profiles:
500,000+
› The corpus was created by Wholi and is one of
the largest corpora used for social profile matching
› While other datasets (Perito et al., 2011; Zhang et al.,
2016) have a larger number of distinct profiles, ground
truth is one order of magnitude larger for our dataset
– 200,000+ compared to ~10,000 items
– This allows training more complex classfiers, including
deep neural networks
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ICCCI 2017
Nicosia, Sep 27th
Deep neural networks for matching
online social networking profiles

10. Dataset
› Ground truth data has been manually entered by users
– It might be incorrect in some cases (entry errors, user misbehaviour)
– Resembles crowdsourced datasets, which are very popular lately to train complex models
› Train and test sets respect the following rules:
1. Train and test sets should contains different online identities (e.g different individuals)
2. The clusters in the training set should have no entries present in the test set in order to
avoid overfitted models
3. Test set has the same distribution for cluster sizes as the train set to provide a relevant
comparison for various sized online identities
› Positive items extracted from about.me accounts, negative ones added
randomly between profiles with similar names, location, etc.
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Deep neural networks for matching
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11. Proposed solution
› Main contribution is using a deep neural network (NN) for matching
online social networking profiles
› NN is able to make use efficiently of both textual features and
domain-specific ones
› Also performed a comparison with other solutions used in previous
studies, employing both unsupervised and supervised methods
› For the unsupervised approach, we first generated the feature vector
for each profile, then applied Hierarchical Agglomerative Clustering
(HAC) using cosine distance
› For binary classification we have a twofold objective
1. Detect whether two profiles refer to the same person and should be matched
(pairwise matching)
2. For the graph of connected profiles discovered in phase 1, compute its
connected components
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Deep neural networks for matching
online social networking profiles

12. Extracted features
› Given a pair of profiles (a, b)
› Domain specific features: distance based measures based on names
(full, first, last) and usernames, matching gender, matching location,
matching company/employer, etc.
› Text-based features
– Computed from all the other textual attributes in a profile (e.g. bio, publications,
interests)
– Used precomputed Word2Vec word embeddings with 300 dimensions,
averaged over all words in a profile
– Also computed cosine and Euclidian distance between word embeddings of the
candidate pair (a, b)
› Features normalization
– Compute the z-scores for each feature
– Whitening using Principal Component Analysis (PCA) in a 25-dimensional
vector space to remove noise
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Deep neural networks for matching
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13. Deep neural network for profile matching
› Given the very large dataset and the recent advances of deep learning, we
propose a deep NN model for profile matching
› Deep NNs should be able to model more complex non-linear combinations of the
different features (domain specific, word embeddings)
› Proposed a model which uses 6 fully-connected (FC) layers with different activation
functions
› The loss function uses cross-entropy, with an added weight for false positives
which contribute 10 times more to the loss
– Penalizes false connections between profiles and counteracts the imbalanced distribution
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Deep neural networks for matching
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14. Deep neural network for profile matching
› The first layer takes as input the features computed for the candidate profile pair
and goes into a larger feature space (612  1024)
› The next two layers iteratively reduce the dimensionality of the representation to a
denser feature space
› The final layers employ RELU activation for the neurons, as RELU units are known
to provide better results for binary classification (Nair & Hinton, 2010)
› Dropout is employed to avoid overfitting
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Deep neural networks for matching
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15. Results
› Experiments performed using an imbalanced test set with one
positive profile pair for 100 negative ones
– Reflects a real-world scenario, where for each correct match between two
profiles, one compares tens/hundreds of incorrect (but similar) candidates
› Table shows B-cubed precision and recall obtained on the test set
› Using same names or similar names as baselines for comparison
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Deep neural networks for matching
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16. Results
› Unsupervised methods (HAC) obtain poorer results than baseline
mainly because cosine is not a good measure for cluster/item
similarity for the proposed feature vectors
› The RF classifier performs well only when domain specific features
are added to the word embeddings
– The large training set limited the number of trees (to 12) in the forest
– RF usually performs poorly when using word embeddings for a pair of
documents (as they cannot compute a more complex similarity function)
› Mini-batch training of NNs allows using larger datasets than for RF
› The deep NN model learns a more complex combination between
word embeddings and domain specific features, grouping profiles
with similar embeddings and similar names
› Deep NN is the only model which can achieve both high recall
(R=0.85) and high precision (P=0.95)
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Deep neural networks for matching
online social networking profiles

17. Results – examples
› Ground truth
– ['twitter/etniqminerals', 'instagram/etniqminerals', 'googleplus/106318957043871071183',
'facebook/etniqminerals', 'facebook/rockcityelitesalsa', 'facebook/1renaissancewoman',
'facebook/naturalblackgirlguide', 'linkedin/leahpatterson’]
› Computed
– [ 'facebook/1renaissancewoman’, 'linkedin/leahpatterson’, 'googleplus/106318957043871071183’]
– ['twitter/etniqminerals', 'instagram/etniqminerals', 'facebook/etniqminerals']
– [ 'facebook/naturalblackgirlguide']
› “Leah Patterson” is an individual who has two different companies “Etniq Minerals” and
“Natural Black Girl Guide”
› Ground truth
– 3 different individuals whose first name is “Tim” and all of them work in IT
› Computed
– ['googleplus/113375270405699485276', 'linkedin/timsmith78', 'googleplus/117829094399867770981',
'twitter/bbyxinnocenz', 'facebook/tim.tio.5', 'vimeo/user616297', 'linkedin/timtio', 'twitter/wbcsaint',
'twitter/turnitontim']
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18. Conclusions
› Proposed a large dataset for matching online social networking
profiles
› This allowd us to train a deep neural network for profile matching
using both domain-specific features and word embeddings generated
from textual descriptions from social profiles
› Experiments showed that the NN surpassed both unsupervised and
supervised models, achieving a high precision (P = 0.95) with a good
recall rate (R = 0.85)
› As far as we know, this result outperforms existing approaches for
profile matching, but further validation is needed (to adapt it for other
datasets and/or use other methods on current dataset)
› Further advancements can be made by training more complex deep
learning models, using recurrent or convolutional networks, and by
adding features extracted from profile pictures
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Deep neural networks for matching
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19. Conclusions
› A similar architecture has been proposed by Google (Convington et
al., 2016) for recommending YouTube videos
› However we have only found this work recently
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online social networking profiles

20. Thank you!
Questions
Feedback
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ICCCI 2017
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Deep neural networks for matching
online social networking profiles

21. Selected references
› Artiles, J., Borthwick, A., Gonzalo, J., Sekine, S., Amigo, E.: Weps-3 evaluation campaign: Overview of the web
people search clustering and attribute extraction tasks. In: CLEF (Notebook Papers/LABs/Workshops) (2010)
› Artiles, J., Gonzalo, J., Sekine, S.: Weps 2 evaluation campaign: overview of the web people search clustering
task. In: 2nd Web People Search Evaluation Workshop (WePS 2009), 18th WWW Conference. vol. 9. Citeseer
(2009)
› Covington, P., Adams, J., & Sargin, E.: Deep neural networks for youtube recommendations. In Proceedings of
the 10th ACM Conference on Recommender Systems (pp. 191-198). ACM (2016)
› Nair, V., Hinton, G.E.: Rectied linear units improve restricted boltzmann machines. In: Proceedings of the 27th
International Conference on Machine Learning (ICML-10). pp. 807-814 (2010)
› Perito, D., Castelluccia, C., Kaafar, M.A., Manils, P.: How unique and traceable are usernames? In:
Proceedings of the 11th International Conference on Privacy Enhancing Technologies. pp. 1-17. PETS'11,
Springer-Verlag, Berlin, Heidelberg (2011)
› Zhang, Y., Tang, J., Yang, Z., Pei, J., Yu, P.S.: Cosnet: Connecting heterogeneous social networks with local
and global consistency. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. pp. 1485-1494. KDD '15, ACM, New York, NY, USA (2015)
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