Bringing social networks into a physical space - social sensing computing

Summary 

This paper used the RFID technology to recognize the user's social cluster from DBLP database. The authors provided a big display screen within a conference that participants can interact with the interpersonal connections. This paper actually consisted with several interesting elements together: 1) the RFID sensor: the RFID sensor is aim to recognize the user approaching the display screen and provide the personified social network graph to user; 2) graph exploration: the display screen provided the zoom in/out function that provide the exploration functions for users to discover the interpersonal relationship; 3) heterogeneous network: the displayed graph contained with the conference/co-citation/co-authorship features, this make the user can discover the hidden relationship or knowledge insides the network.

The experiment result showed a positive feedback from users, but some of the issues attract my eye: 1) limited user usage: only few of the conference participants actually used the system, fewer people used more than one time; 2) the cold start problem: some junior scholar may not have publication or their publication is not listed inside the DBLP database; 3) the privacy issue that display your personal social network in a public view display screen; 4) the cost and purpose to deploy RFID: the usage of the RFID tag is not significantly necessary.

This idea is interesting to make the conference with more fun and entourage people to explore the social interaction during the event. But I think this tool should be more personalize and privacied. For instance, to display the result with a personal device, e.g. cell phone. In other way, the design of the graph is not highlighting the meaningful information for users. To show everything on screen is almost equal nothing for users. This might be the reason the user return rate is low. These issues would be valuable for the future application.

Besides, the RFID tag implied a research subject about the social sensing computing. With the developing of wearable devices, there are more and more sensor, tag or device that with the potential to load the user data for multiply computing task. For research purpose, to gather all of these data is critical, also, difficult. However, there are many alternative way to "simulate" the scenarios. For instance, a QR code, a reference number or mobile phone built-in function (e.g. GPS, Bluetooth). All of the technologies are interesting, but also require many developing efforts. Nor sure if this could be our expertise to do all these things by our own?

Reference

1. Konomi, Shini'chi, et al. "Supporting colocated interactions using RFID and social network displays." Pervasive Computing, IEEE 5.3 (2006): 48-56. Link

Summary for two papers of facilitate the conference by social games

Summary 

To help conference attendees gain social capital or expand social networking is always an interesting research topic. One of the direction is by "social games". The paper from [1] and [2], both design a social game to facilitate people social interaction inside a conference. In  [1], the author designed a game requires 2-6 people to communicate on a puzzle of ball and hole location matching. This is a tool for "ice-breaking" between the conference attendees through the teamwork procedure. In [2], the author discussed an approach to gain community retention by the social game. They build up a cell phone app that supports a collaborative function on the task. The users can solve problems insides the game with others, as a community. The author argued, this app encourages user to improve their network connectivity. Furthermore, this app may be used inside the class to increase the class retention rate for minority groups.

Both of the papers proposed an interesting idea about social game. However, I think the evaluation would be an issue to support the above claims. In [1], the author proposed a questionnaire for the game players. Based on the response, the users indicated the communication and team-work function are the most important election for them. The evidence to support the game usefulness in helping users to "making friends fast" is not strong. In [2], the authors only present the evaluation plan about how the social game is helping to build a community. The experimental data are still lacking.

I think the research question can be more specific classified as 1) cold-breaking; 2) social interaction and engagement; 3) social recommendation; 4)social networking ; 5) teamwork and communication and 6) community formation and retention. For each of them, the experimental design should be varied. Some of the aspect is hard to find a ground truth to prove the model/game/app effectiveness. For instance, if the user talk to each other more due to the apps? It is not easy to compare the talk frequency before/after the game play. Hence, an experiment design for certain research questions is critical. Some of the ideas: 1) A/B testing to different group of users; 2) quick questionnaire/feedback insides the game; 3) clicking/bookmarking/friendship behavior analysis, etc.

Reference

[1] Evie Powell, Rachel Brinkman, Tiffany Barnes, and Veronica Catete. 2012. Table tilt: making friends fast. In Proceedings of the International Conference on the Foundations of Digital Games(FDG '12). ACM, New York, NY, USA, 242-245. DOI=10.1145/2282338.2282386 http://doi.acm.org/10.1145/2282338.2282386

[2] Samantha L. Finkelstein, Eve Powell, Andrew Hicks, Katelyn Doran, Sandhya Rani Charugulla, and Tiffany Barnes. 2010. SNAG: using social networking games to increase student retention in computer science. In Proceedings of the fifteenth annual conference on Innovation and technology in computer science education (ITiCSE '10). ACM, New York, NY, USA, 142-146. DOI=http://dx.doi.org/10.1145/1822090.1822131

ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations

Summary

This paper is mainly about the solution of item cold start problem in the real world recommender system. In an on-line recommender system, the cold start problem is caused by lacking of the historical data to generate meaningful suggestions. In other words, the cold start problem can be treated as the user or item that newly enter into the system. In general, the cold start problem was solved by include new context or content features. However, in this paper, the author focus on the specific solution for collaborative filtering (CF). Their idea is conducting a small scale user experiment to those users who might interested in the new item. The user interaction within the experiment can be used as a reference for the new item recommendation.

The major challenge behind this idea is: Who is the target user? In this paper, they proposed a novel algorithm "ExcUseMe". The authors assumed the user is randomly visited the online recommender system. In this continuing user stream, the system required to determine who should be included in the new item experiment. The approach can be divided into two stages: 1) the learning phase; 2) the selection phase. In 1), the algorithm is computing the likelihood of the user provide feedback of the item. In stage 2), the user is selected by their likelihood ranking. The vector similarity between the users are also considered in a semi-greedy manner.

The experiment simulated the real world on-line setting, they sampled the n% users as selection pool. The performance was evaluated by RMSE (Root-mean-square deviation) metric for three large scale datasets. The baseline was compared with Random, Frequent Users, Distance and Anava approaches. The experiment result indicated the proposed ExeUseMe algorithm outperform all the baseline models, moreover, with a lower computational cost. The experiment result also pointed out the importance and contribution of positive feedback from users. In other words, the key to drive this method as a meaningful output is the user participants.

This paper highlight a new approach for CF to solve the cold start problem. Besides, this approach is also practical that can be adopted into the real world on-line system. The main contributions of this paper are 1) the real-world user selection method; 2) efficiency algorithm and 3) the high model performance in most of the scenarios. There are some comments in this paper: 1) this algorithm is valuable in an on-line experiment setting, but the experiment is simulated with off-line setting and dataset. A further simulation experiment can better demonstrate the advantage of this approach; 2) the data filtering section limited the user with between 20 - 300 ratings. This setting reduced the probability to provide useful suggestions for long-tail items; 3) In real-world recommender system, the small amount of user feedback lead to the result of a recommendation. There might be some incentive to inverse control the experiment result in some ways.

Reference

Michal Aharon, Oren Anava, Noa Avigdor-Elgrabli, Dana Drachsler-Cohen, Shahar Golan, and Oren Somekh. 2015. ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations. InProceedings of the 9th ACM Conference on Recommender Systems (RecSys '15). ACM, New York, NY, USA, 83-90. DOI=http://dx.doi.org/10.1145/2792838.2800183

Relescope: An Experiment in Accelerating Relationships

Reading Summary

To help people to digest the information is always an interesting research question. In this paper, the author tried to produce a 1-2 page short report to the conference attendees based on their previous works. This report provided a meaningful relationship network of the conference and aim to better determine the suitable social activity during the conference, for example, to recognize and talked to people. The author sends out the questionnaire to examine the validity of the report. The survey showed the effeteness of this application. Moreover, the newcomer of the conference is benefited more than the senior participants.

This implication reminds a fundamental way of human to digest information: the "device" to provide personalized information to users. The paper is published in the year 2005. At that time, since the cell phone is not popular with the public yet, the author sends out the application result by paper. Sometimes, the small piece of paper might be useful and convenient for people to access and carry the information during the conference. This is the reason, until today, the paper handout is still the most necessity in an academic conference.

The paper-based approach is with the natural limitation - update the information and interaction. Besides, it is also hard to collect user behavior or feedback based on this approach. With the most popular on mobile and wireless technology, it is possible for people to stay online all day. The social media, e.g. Facebook, Twitter and Linkedin, start to grab the attention of participants of the physical environment. People are now interacting in physical and virtual. The exploration of social community behavior is a popular research topic today. For instance, the human behavior/interaction on Twitter during a conference.

The future study might be focused on the personalized recommendation, e.g. Conference Navigator, tried to provide a personalized application interface for conference participants. However, there is still an unanswered question about how to help the newcomer of the conference to better into the environment and to their future academic career. More precisely, to promote a meaningful social connections. The way to evaluate the effectiveness of social connections is a challenge here.

Reference:

1. Farrell, S., Campbell, C., and Myagmar, S. (2005) Relescope: an experiment in accelerating relationships. In:  Proceedings of CHI '05 extended abstracts on Human factors in computing systems, Portland, OR, USA, ACM, pp. 1363-1366, also available athttp://dx.doi.org/10.1145/1056808.1056917.

A talk summary of "Professional Research Opportunities for Ph.D. Students in MSR"

Summary 

This is a talk summary of [1], the speaker mentioned:

1. It is a new era of Microsoft research with new CEO and board member. The research lab now more follows and support the three pillars of the new company agenda, compare to the time of bill gate. There are three pillars of MSR (Microsoft Research Lab): Productivity, Smart cloud and Personal computing, e.g. health care. The mission statement is, like academics to pursue knowledge, looking for new ideas and innovation from MSR to the corporation. 
2. Two constraint of applying a funding for researcher: the budget from the Congress is shrinking, also the current political situation in Congress: more young assistant that might lack of the passion on sciences and more focus on sweet issues, e.g. education bills and subsidy. 
3. The difference between the research lab and academia: in MSR, you don't need to worry about the research funding for graduate students, you have colleagues and interns. You will get 12 months salary and the research lab inspires the fundamental research questions. It is a bottom up style; In academia, you will need more writing proposals, sometimes it is hard to get money to support your research. 
4. MSR cares the career professional development. Their own career. Welcome to co-work with other teams. Seek to impact to the world. Get your knowledge or innovation on the Microsoft product. Get things into practical products. 
5. In big-data era, there is an advantage to work in research lab: the massive data inside the company that cannot share outside of the world. This would be benefited with the research that doing speech, translation, machine learning and some more research subjects. 
6. Microsoft also puts things out of open source. Not like IBM, who much more value patents, but MSR respects more to academic value. When you interview a position, what is the cultural difference in there? This is a question worth to ask. 
7. MSR evaluated the research by their impact. They expected you as the expert in some fields, also bring value to companies. Change the peer-review rule two years ago, not to only counting your publication. No tenure track. 
8. MSR also encourage to deliver the research to start up. Welcome that kind of people to bring in more different genes. 
9. No matter you are in industry or academia, you will learn how to expose your work in confidence.  Who does your audience you focus on? E.g. how to explain your work to WSJ reporter? This is a story telling absolute you need to learn. Also, to get funding is important in academic, good to know who control the faucet (money). 

Thoughts

I feel the goal of Microsoft from the new board of directors is clear: productivity. All the products and services from MS will be around this core. The MSR would not be an exception. The vice-president speech clearly points out the three pillars of the mission statement. It is hard to avoid the demanding from boards about delivery the research, innovation to the value of the company. In another way, this might be a chance to bring the research into a real world products or services. However, is the mission goal of company will crowd out the support of fundamental researches? This might be an another question worth to ask. Besides, to leverage the huge dataset from the MS products and services would really be a beneficial for researchers, e.g. the social network from Hotmail, the behavior patterns from Office 365 or the user generated data from Windows. All the data is valuable for researcher to proceed the experiments. This might be also a disadvantage and challenging for data sciences researchers in academia. Any thoughts?

Reference

1. Professional Research Opportunities for Ph.D. Students, Jeannette M. Wing, http://halley.exp.sis.pitt.edu/comet/presentColloquium.do?col_id=8982

Technology policy research of Korea - mobile platform and network neutrality

Summary

The technology policy from the government always plays a critical role in the industry. In the research of [1], the authors intended to measure the efficiency difference before and after the platform standardization policy formulation and implementation (WIPI). They adopted the idea of "efficiency frontier"to measure the mobile company performance difference. According to their empirical finding, the government-led mobile platform standardization policy is negatively affecting the mobile companies' efficient, compare to the company independent to the mobile network operators. The author suggested the government should play a supporting-role in the policy regulation.

In paper [2], the author discussed the network neutrality effects on new internet application services. They proposed a simulation experiment to examine the service diffusion in different network regulation settings. They found the "more latency sensitivity and broader bandwidth services have displayed a higher willingness to pay (WTP) for high-priority Internet services" which accordance to with the assertion of network provider have the incentive to charge additional fees on certain services. However, they further explored the diffusion effect under government regulation. They found the discrimination from network provider might hurt the growth of new-coming internet service diffusion. They also suggested the government need to take in action to protect the new innovative service in early stage.

Thoughts

The research of technology researches is full of the regional differences. However, this is a good way to reference the experience of other countries. For the first paper, the authors imply an interesting research question: what is the role that government should play in the new mobile era? The technology is rapidly changing time by time. It is pretty hard for a government to propose a complete and flexible policy for the new industrial business. The idea of WIPI is clear, the Korean government intends to construct a universal mobile platform inside Korea. If the regulation succeeds, they can have a giant eco-system to promote their mobile application industry, as a game rule maker. The idea is similar to Japan mobile platform that adopted more localize special specifications. However, even larger market in Japan can not resist the platform competence from Apple ios and Google Android. That is why the government-led standard regulation is hard to compete with the other two industries-led platforms. There are more economic issues behind the scene, this could be another interesting research subject.

The dispute of network neutrality is another debate between network service providers and operator. There is a famous case from Netflix, a high latency sensitivity and broader bandwidth on-line video streaming services. The story is ended in Netflix pays the extra access fee to network operator Comcast. This action is a revenue worth decision for Netflix, but might be a huge barrier for some new internet service, as the paper [2] claims. The FCC is trying to forbid the discrimination charge from network operators, but this rule also arouses great controversy on the government internet control issue. The multiple stakeholder in this game makes this dispute continual. The simulation approach of this paper would be a way for us to examine the internet control policy.

Reference:

1. Hongbum Kim, Daeho Lee, Junseok Hwang. (2016). Measuring the Efficiency of Standardisation Policy Using Meta-Frontier Analysis: A Case of Mobile Platform Standardisation, International Journal of Mobile Communications.
2. Lee, Daeho, and Hongbum Kim. "The effects of network neutrality on the diffusion of new Internet application services." Telematics and Informatics 31.3 (2014): 386-396.

An summary review of three recommendation systems for academia.

Summary

The recommendation system is a way to help users to better retrieval or digest the ubiquitous enormous information. In [1], the authors tried to build up a committee candidate recommendation system to help conference organizer. This is one sub-domain of expert finding studies. They adopted the social network of the program committee (PC), publication history and topical expertise matching to generate a list of potential committee members. They found the three prediction features are all useful to provide a useful recommendation result. 

In [2], the authors intended to help scholars to choose the suitable journal to contribute their works. They built a system to ask user input their publication title, abstract and domain tag. Based on the input information, they proposed an information retrieval model to generate the high-similarity journal list. This is basically a content-based approach using BM2.5 algorithm.  This study focus on the publisher of Elsevier that might favor the paper in relevant discipline. If the sample paper is enough, the recommendation performance is valid. 

In [3], this paper helped user to search the relevant research publication based on their publication reference list. The idea was to enhance the search ability rather than keyword-based search. This could be treated as an extension of information retrieval research. They proposed a system to ask user to input a list of reference paper and built up the citation graph. This is a graph-based approach to do the recommendation system. The idea behind this system implies the potential of secondary or more search in different applications. 

Thoughts

The idea of recommendation system could be applied to several research questions. However, it is pretty difficult to examine the effectiveness of the recommendation result. There are three main directions to solve this issue: 1) ground truth[1][2]; 2) user study[3]; 3)domain expert (e.g. knowledge ontology, expert review). 

1. The ground truth approach: is widely used in many different data mining researches. The study compared the proposed model with the previous real world user generated data. This is a way to claim the effectiveness of your model or system. However, there are two issues here. First, in some research topics, it is pretty hard to get the ground truth (or maybe not re-producible). Second, this is encouraging research to "fit" model into the exist dataset. The model might not fit the new growing data or features. 
2. The user study approach: is another widely used approach in cross-domain studies. For example, the psychology that study in human behavior would hire the users to do experiments in a closed, controlled environment. In another way, the proposed recommendation system would record the user feedback to examine or improve the system effectiveness. However, the cost of user study is high, either to hire participants or build up a system with large number of users. Moreover, the closed, controlled experiment might not fit the sense of ecologically. The conclusion might lack of the utility in the real world. 
3. The domain expert: is an approach that more used in social science. They invited the domain expert to verify the result. Based on the reliability of the domain experts to prove the effectiveness of the research finding. In another way, some research focused on the ontology building of domain knowledge. This is a way to transfer domain knowledge to a recommendation model. However, the cost to invite domain expert is high, moreover, the comments from experts might be an inconsistency or contradiction. The same issue also exists in domain knowledge building, to construct a complete and rigorous logic, ontology is still a challenge research problem.

Reference:

1. Han, Shuguang, Jiepu Jiang, Zhen Yue, and Daqing He. “Recommending Program Committee Candidates for Academic Conferences.” In Proceedings of the 2013 Workshop on Computational Scientometrics: Theory & Applications, 1–6. CompSci ’13. New York, NY, USA: ACM, 2013. doi:10.1145/2508497.2508498.
2. Kang, Ning, Marius A. Doornenbal, and Robert J.A. Schijvenaars. “Elsevier Journal Finder: Recommending Journals for Your Paper.” In Proceedings of the 9th ACM Conference on Recommender Systems, 261–64. RecSys ’15. New York, NY, USA: ACM, 2015. doi:10.1145/2792838.2799663.
3. Küçüktunç, Onur, Erik Saule, Kamer Kaya, and Ümit V. Çatalyürek. “TheAdvisor: A Webservice for Academic Recommendation.” In Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, 433–34. JCDL ’13. New York, NY, USA: ACM, 2013. doi:10.1145/2467696.2467752.