User Modeling

 

What is User Modeling?

User modeling is the subdivision of human–computer interaction (HCI) which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way [1].

A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling [2].

What are some research resources as a starting point?

On top of the definitions regarding user modeling, depending on what kind of characteristics we are focusing on,  there has been many research going on in different domains such as social media and e-learning. 

In the following, we list some of the tutorials and surveys that might be a good starting point based on different kinds of characteristics and domains you might be interested in. If you find other surveys or tutorials that are interesting and important but missing from the list, you can leave a comment on that.

Surveys      

• [General] "A Survey on Representation Learning for User Modeling", Li, Sheng, and Handong Zhao, IJCAI, 2020. https://www.ijcai.org/Proceedings/2020/0695.pdf
• [Social Media] "Extracting, Mining and Predicting Users’ Interests from Social Media", Zarrinkalam et al. Foundations and Trends@ Information Retrieval, 2020. http://dx.doi.org/10.1561/1500000078
• [Social Media] "Social media-based user embedding: a literature review", Pan, Shimei, and Tao Ding, IJCAI, 2019. https://arxiv.org/pdf/1907.00725.pdf
• [Social Media] "Social Profiling: A Review, Taxonomy, and Challenges", Bilal et al. Cyberpsychology, Behavior, and Social Networking, 2019. https://doi.org/10.1089/cyber.2018.0670
• [General] "A Survey of User Profiling: State-of-the-Art, Challenges, and Solutions", Eke et al., IEEE Acess, 2019.  https://doi.org/10.1109/ACCESS.2019.2944243
• [Social Media] "Inferring User Interests in Microblogging Social Networks: A Survey", Piao, Guangyuan; Breslin, John G. User Modeling and User-Adapted Interaction (UMUAI): 55, 2019. https://doi:10.1007/s11257-018-9207-8
• [e-Learning] "Learner modelling: systematic review of the literature from the last 5 years", Abyaa, Abir et al., Educational Technology Research and Development, 2019. https://doi.org/10.1007/s11423-018-09644-1
• [Social Media] "Emerging trends in personality identification using online social networks—a literature survey", Kaushal, Vishal, and Manasi Patwardhan, ACM Transactions on Knowledge Discovery from Data (TKDD) 12.2 (2018): 1-30, https://doi.org/10.1145/3070645 
• [Social Media] "360◦ User Profiling: Past, Future, and Applications", Farseev et al. ACM SIGWEB Letter, 2016. https://doi.org/10.1145/2956573.2956577
• [General] "User profiling-a short review", Cufoglu, Ayse, International Journal of Computer Applications, 2014. https://doi.org/10.5120/18888-0179
• [Social Media] "A survey of user modelling in social media websites", Abdel-Hafez, Ahmad & Xu, Yue, Computer and Information Science, 2013. https://doi.org/10.5539/cis.v6n4p59
• [General] "Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia", IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Frias-Martinez, E., Chen, S. Y., & Liu, X., 2006. 10.1109/TSMCC.2006.879391
• [General] "User Modeling: Recent Work, Prospects and Hazards", Alfred Kobsa, Human Factors in Information Technology, 1993. https://www.ics.uci.edu/~kobsa/papers/1993-aui-kobsa.pdf

Tutorials

• [Social Media] "User Interest Mining from Social Media", IJCAI 2020.
• [Social Media] "User Interest Mining from Social Media", CIKM 2020. https://doi.org/10.1145/3340531.3412167
• [Social Media] "Social User Interest Mining: Methods and Applications", KDD 2019. https://doi.org/10.1145/3292500.3332279
• [Social Media] "Extracting, Mining and Predicting Users’ Interests from Social Networks", SIGIR 2019. https://doi.org/10.1145/3331184.3331383
• [Social Media] "Semantics-aware Techniques for Social Media Analysis, User Modeling and Recommender Systems", UMAP 2016. [slides]
• [General] "User Modeling in Telecommunications and Internet Industry", KDD 2015. [video]

References

1. Fischer, Gerhard (2001), "User Modeling in Human-Computer Interaction": Fischer, Gerhard (2001), "User Modeling in Human-Computer Interaction", User Modeling and User-Adapted Interaction 11: 65–86. doi:10.1023/A:1011145532042 ↩︎
   
2. Piao, Guangyuan; Breslin, John G. (2018). "Inferring User Interests in Microblogging Social Networks: A Survey". User Modeling and User-Adapted Interaction (UMUAI): 55. arXiv:1712.07691. doi:10.1007/s11257-018-9207-8↩︎
   

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User Modeling in Telecommunications and Internet Industry (KDD 2015)

& another video: https://ieondemand.com/presentations/user-modeling-in-social-media-and-big-data-

Points:

• User Modeling is about:
• who U R?
• where R U?
• what's next?

• User Modeling is for machines, not for human decision maker
• you cannot put human bias for what data to collect (as much as possible, AMAP)
• human design how to collect AMAP
• require a good business model (Nokia competition data does not reflect industry reality) 

• User Modeling
• collaborative, crowd intelligence
• continuous incremental (life long) learning (closed loop)
• feature engineering is important
• privacy centric (not afterthought)

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EKAW2016 Travel Report

From 19-24, November, I attended 20th International Conference on Knowledge Engineering and Knowledge Management at Bologna, Italy. It's a biannual conference on Knowledge Engineering along with the K-CAP conference.

There were around 150 participants from worldwide. Regarding submissions, there were 226 abstracts which resulted in 171 final submissions in total. 539 reviews were submitted for those papers and 42 out of 142 research papers have been accepted. Based on further quality assessment, the organizers also divided 42 papers into long presentations (17.3%) and short presentations for presentations during the conference.

Keynotes:

The first keynote was given by Chris Welty from Google research. He talked about how current AI systems are losing information with one label ground truth for training themselves (e.g, a song might be in different genres or not in the options you provided for getting ground truth data with a survey). He pointed out current simplified world for AI, which consists of black and white, while the reality is much complex. To achieve better ground truth labeling, he also introduced solutions such as using the wise crowd with diversity-enabled labeling for training AI systems.

The second keynote was given by Francesca Rossi from IBM research. She talked about AI has the capabilities to make sense of the huge volume of data (text, images, videos, etc.) that surrounds us in our everyday private and professional life, and to transform it into knowledge to be exploited to make better and more informed decisions that could help solving global societal problems such as those in healthcare, transportation, and climate. To achieve these goals, and in order to fully exploit the potential of AI, we need to build intelligent machines that behave ethically and create symbiotic partnerships with humans. So rather than considering/making AI for Decision Making Systems, we need to consider/make it as Decision Support Systems.

The conference sessions are very diverse, from data management to NLP as well as Entity Recognition, Crowdsourcing, ontology related topics etc.

My presentation:

I presented a User Modeling work considering different dimensions studied in the literature for investigating their synergetic effect on User Modeling.

EKAW2016 - Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter from GUANGYUAN PIAO

The organizers also did a good job on proceedings, which were available before the conference:

• http://link.springer.com/book/10.1007/978-3-319-49004-5

UMAP2016EA

Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOCRecommendations

[UMAP2016 submission by Guangyuan Piao and John G. Breslin]

About

This post provides supplemental material and information about the poster "Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations: a first look". Available online: 

Poster:

UMAP2016EA - Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations from GUANGYUAN PIAO

Dataset 

name	number of records	description
users.sql	5668	5668 learner profiles from LinkedIn who have been taken any Coursera MOOCs
coruseRecordsV1.sql	15744	course records extracted from user profiles
eduExperience.sql	11085	educational experience of learners
workExperience.sql	32801	work experience of learners
skills.sql	159291	skills of learners

Descriptive statistics: the dataset is about analyzed MOOC learner profiles from LinkedIn, which consists of 15,744 MOOC entries from 5,668 professionals. Each professional took 3 courses on average with the majority of learners (87%) having less than or equal to 5 MOOCs. Interestingly, the learner with the largest number of MOOCs had 114 of them. The distribution of genders and degrees of learners is as below:

If we assume that course entries in LinkedIn are courses that have been completed by users, the distribution of degrees are similar to the study [1] which provides the distribution of learners who completed their course.

Verified certifications: Instead of just taking MOOCs on Coursera and getting statements of accomplishment, learners can also purchase verified certifications for some courses that meet certain criteria. A verified certification provides proof that learners have completed their online courses. In such cases, varied certifications can also be added to LinkedIn parallels with their varied serial numbers. We found that around 26% of certifications in our collected profiles are verified while 74% of the certifications are unverified. 

Course tracks. We found that course tracks can be identified by exploring learning activities of users in the OSN. Formally, we can define a course track as a set of courses that were taken together more than n times where n is a threshold. The course relationships can be represented by weighted undirected networks like in the figure below. 

Nodes denote courses and the ties among courses denote the frequency of two courses taken together. In this context, a course track is a clique (or complete graph that has an edge joining each pair of nodes) within the course relationships network, with the weight of each tie in the clique is higher than the threshold n. Course tracks can be constructed based on the cliques within the course relationships network. As one might expect, the higher of the value n, the stronger the relationships a course track must hold with less number of cliques meeting the criteria. Indeed, 60 maximal cliques (a clique in maximal if it cannot be extended to a larger clique) can be found with a threshold of 10 while 16 maximal cliques can be found with a threshold of 20. 

We evaluated these tracks and found that two of the course tracks provided by Coursera can be identified in those cliques through this approach. A course track, called a specialization in Coursera, is a targeted sequence of courses from an institution taken together to earn a specialization certificate. The first course track from Coursera is a specialization of "Data Science" which consists of 9 courses from Johns Hopkins University, and the second one is a specialization of "Business Foundations" provided by the University of Pennsylvania. In practice, these ground truth course tracks can also be used for identifying the threshold n, which is the highest value that does not break the ground truth course tracks. In our case, 27 maximal cliques can be found including the two golden truth course tracks with the value of 13 for the threshold. Interestingly, when we look at the maximal clique that contains the "Data Science" course track (Figure 2), we found that "Machine Learning", "Introduction to Data Science" and "Computing for Data Analysis" are also being taken frequently with 9 courses in the "Data Science" course track in practice. This indicates that new course tracks can be constructed on top of existing tracks by exploring learning activities of users from the OSN.

[1]. T. Balch. MOOC student demographics. Retrieved Apr, 28:2013, 2013.

UMAP2016S

Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations

[UMAP2016 submission by Guangyuan Piao and John G. Breslin]

About

This post provides supplemental material and information about the paper "Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations". 

Abstract

In this paper, we study if reusing Google+ profiles can provide reliable recommendations on Twitter to resolve the cold start problem. Next, we investigate the impact of giving different weights for aggregating user profiles from two OSNs and present that giving a higher weight to the targeted OSN profiles for aggregation allows the best performance in the context of a personalized link recommender system. Finally, we propose a user modeling strategy which combines entity- and category-based user profiles using with a discounting strategy. Results show that our proposed strategy improves the quality of user modeling significantly compared to the baseline method.

Slides:

UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations from GUANGYUAN PIAO

About.me Dataset

Users tend to have multiple social identities in different OSNs [1]. To retrieve the ground truth data (i.e., users who are using both Google+ and Twitter), we obtained OSN accounts of users from about.me. About.me is a personal web hosting service, which offers registered users a simple platform from which to link multiple online identities, relevant external sites (e.g., personal homepage), and popular OSNs such as Facebook, Twitter, Google+ etc. We started from a set of randomly returned about.me accounts retrieved from about.me API15 and then gradually extended this set in a snowball manner. After all, we crawled 247,630 public profiles pages from about.me during December 2014 that have at least two external links. Two irrelevant external links to OSN identities (i.e., relevant external sites and RSS feeds that users added) were removed.

Figure 1. OSN co-occurring network in about.me dataset

As a result, there are 29 different communities in our dataset (see Figure 1). In Figure 1, the ties between OSNs show the co-occurrence frequency of two social networks in the profile pages of users.

The portion of users having three OSNs is the highest (22%) followed by 20% and 18% for those having four and two social networks, respectively. Over half (60%) of people have 2-4 social networks and each person participates in 4.48 OSNs on average. In our dataset, the number of different OSNs (29) and the average number (4.48) that each person participates in are both higher than the numbers from the previous study [14], which are 15 and 3.92 respectively.

Dataset for our study 

As we were interested in analyzing aggregated user profiles from Twitter and Google+, we randomly selected 480 active users from about.me dataset who had been using both OSNs. We extracted their UGC from Twitter and Google+ as well as all links shared with those UGC using our user modeling framework. All DBpedia entities within UGC and those within the content of each link were retrieved using the framework. The numbers of entities extracted from Twitter and Google+ profiles of users are displayed in Figure 3. As we can see from the figure, a greater number Of entities can be extracted from Google+ activities.

Figure 2. The number of entities extracted from Twitter and Google+ profiles of users

References

[1]. J. Liu, F. Zhang, X. Song, Y.-I. Song, C.-Y. Lin, and H.-W. Hon. What's in a name?: an unsupervised approach to link users across communities. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 495-504. ACM, 2013.

User Modeling in Internet & Social Web Industries

another speech from the same speaker:
https://ieondemand.com/divisions/big-data/presentations/user-modeling-in-social-media-and-big-data-

Review of 2015

Research

The year 2015 is finished and I've been in Ireland and Insight for 1 year and 6 months. Doing research here gives me quite different experience than previous research experience, and it poses good opportunities as well as challenges for myself.

Independent:  

You have to grow up and be able to do your research (not projects) with your ideas and opinions, and conduct experiments by your own. I remembered the seminar I participated at the beginning of the PhD journey and the speaker described our academic supervisor as advisor since it is more appropriate. That means our advisor is who giving advices for your research but not who telling you every step you should move forward, and usually our supervisors also too busy to do so. 

At first, I could not start own research and conduct experiment by myself, and there was always uncertainty about myself and I realized the way I've been trained always was "supervised" by others. It reminds me the time in South Korea when I was a master student as well as an employee in a company where I received a lot of things to-do every day from senior members. In contrast, I did not receive any call here, and all communication has been done through emails which is still a surprising fact for me. Thanks to God, even I have a lot of improvements to achieve, I have started the research, with advice from my supervisor.

I started to recognize the statements from (So you want to do a PhD from Open University) that a PhD is confirming your "research independence", i.e., you have to demonstrate that:

1. Ability to do research by yourself, rather than simply doing what your supervisor tells you
2. Awareness of where your work fits in relation to the discipline, and what it contributes to the discipline
3. Mature overview of the discipline

Insight Centre: 

There have been many changes for Insight@Galway which was formally well known as DERI. Our former director Prof. Steffan Decker moved to Germany and we have new director Prof. Dietrich Rebholz-Schuhmann. Interestingly, many researchers, including PhD students, Postdocs moved to Germany as well. There are many career paths for graduates from here including academic positions as well as industry ones, or even some of them start running own startups etc.

Conference

After several attempts for conferences, I've published two full papers in JIST2015 and SAC2016, and I found that it is really important to publish or try to publish your results in any conference or journal to get started, and get feedbacks from the experts. In my previous experience, I've been recommended do not to read and present a conference paper for a seminar during previous studies. However, here, one thing I love is that top conferences have the same importance to top journals. There is an interesting article to read if you have the same wondering: https://homes.cs.washington.edu/~mernst/advice/conferences-vs-journals.html
At the end of the year, I submitted a paper to ESWC2016 which has very interested tutorials for me http://2016.eswc-conferences.org/program/workshops-tutorials and hope I will have an opportunity to attend it:). Another conference I'd like to participate is UMAP2016 which is also highly related to my research. So... Fingers crossed for the upcoming new year.