Research

Social networking services (SNS) play a critical role in helping people to connect with new friends, share opinions with likeminded people, and stay in touch with older acquaintances and colleagues. It allows users to share ideas, posts and digitals content with people in their networks, which has been widely adopted as a tool for day-to-day personalized information access and decision making. While the popularity of SNS consistently rises, it calls for expanding our understanding of exchanging knowledge, initiating new educational methods, and encouraging social interactions, all of which reinforce the importance of establishing the accountability in distinct use cases. To fulfill this need, I adopt data-driven and user-centered approaches to design, build and evaluate intelligent systems.

I am a human-computer interaction researcher with a background in using the data-driven approach to web system development, particularly recommender systems, online educational systems, and social media. My research strives to develop data-driven approaches to utilize multi-relevance data in human-centered applications efficiently. So far, I have conducted social network analysis (SNA) with regards to scientific productivity [10, 13], international relationships [14], underrepresented groups [12] and social capital [1]. My work continues by building social recommender systems to reduce the difficulty in human decision making [9, 11] and to overcome social information overload by leveraging intelligent interfaces to enhance recommendation diversity [3, 4, 6, 7], controllability [3, 4, 8] and explainability [2, 5, 8]. These projects have led to 10+ peer-reviewed publications, grants, and usage by thousands of people from over 16 countries and at 10 academic conferences.


Social Network Analysis for International Relations

Network analysis has brought new perspectives to studying emergent structures in different contexts, e.g., international relations. Prior work on international relations networks has mostly relied on data derived from formal alliances and trade flows, which barely captures the rapidly evolving state of international relations due to globalization and recent advances in information technology. In this work [14], I proposed a novel research design that aims at capturing “real-time” international relations through news reporting data (~1 million). I collected worldwide news on a daily basis, and characterized the relationship between any two countries by analyzing the similarity to their news content. I presented the empirical results based on news about the APEC (Asia-Pacific Economic Cooperation) CEO Summit in 2013, revealed interesting and meaningful international relations among member countries.



Figure 1: the networks of country-to-theme, most countries mentioned the trade and growth issues.

Social Recommender Systems for Local Businesses

On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In this project [9, 11], I aim to build a local business recommendation system to provide suggestions for future business collaborations. Based on historical data from Yelp that shows two businesses being reviewed by the same users within the same season, I was able to identify businesses that might attract the same customers in the future, and thereby provide them with insights into potential collaborations. I presented the finding that the sharing review network is a useful feature to predict the future collaborative marketing opportunities between businesses.



Figure 2: Location-based recommendation in Pittsburgh.

User Intelligent Interfaces and Social Recommenders

Put the User in Control

A social recommender system aims to provide useful suggestions to the user and prevent the social overload problem. Most of the research efforts address pushing high relevant items to the top of the ranked list, using a weight ensemble approach. However, I argued that the “learned” static fusion is insufficient for different specific contexts. In this project [3, 4, 8], I developed a series of visual recommendation components and a control panel for the user to interact with the recommendation results at an academic conference. The system (shown in Figure 3) offers better recommendation transparency and user-driven fusion of recommended sources. The experiment result shows that the user did fuse the different recommended sources and exploration patterns among tasks. The post-study survey is positively associated with the effectiveness of the system and quality of the explanation function. This finding shed light on future research questions about the design of a recommender system with human intervention and the interface beyond the static ranked list.




Figure 3: Relevance Tune - a visual interface with user-driven control function and meaningful visual encoding.

Break the Filter Bubble

Increasing diversity in the output of a recommender system is an active research question for solving a long-tail issue. Most of the current approaches have focused on ranked list optimization to improve recommendation diversity. However, little is known about the effect that a visual interface can have on this issue. This project [3, 4, 7] shows that a multidimensional visualization (shown in Figure 4) promotes diversity of social exploration in the context of an academic conference. My study showed a significant difference in the exploration pattern between the ranked list and visual interfaces. The results showed that a visual interface could help the user explore a more diverse set of recommended items.



Figure 4: Scatter Viz - a dual social recommender interface, which includes a ranked list and visual scatter plot components.

Controllable and Explainable Artificial Intelligence

Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to a user. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work [2, 5], advanced visualization has been explored as an approach to increase transparency and enhance the user experience. In this project, I attempted to further increase the transparency of a recommendation process by augmenting an interactive hybrid recommend interface with several types of explanations.

I evaluated the behavior patterns and subjective feedback by a within-subject study of three academic conferences. Results from the evaluation show the effectiveness of the proposed explanation models. The post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of controllability. This finding shed light on future research questions about how to design human-scale explanations in social recommenders.



Figure 5: A subset of visualizations for explaining the social recommendations.

User-Centric Evaluation

The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this project [3, 4], I presented two attempts at creating a visual diversity-enhanced interface (Figure 3 & 4) that presents recommendations beyond a simple ranked list. My goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. I showed that the users examined a more diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences will be discussed that shed light on avenues for future interface designs.


Figure 6: the user experience effects on proposed interfaces through a structural equation model (SEM) analysis.


References

1. Chun-Hua Tsai. 2019. Building Social Capital at Academic Conferences: Diversity Exposure in Social Recommender (Manuscript submitted for publication)

2. Chun-Hua Tsai. 2019 Explaining Recommendations in an Interactive Hybrid Social Recommender (Manuscript submitted for publication)

3. Chun-Hua Tsai and Peter Brusilovsky. 2018. Exploring Social Recommendations with Visual Diversity-Promoting Interfaces. ACM Transactions on Interactive Intelligent Systems (TiiS) (To appear).

4. Chun-Hua Tsai and Peter Brusilovsky. 2018. Beyond the Ranked List: User-Driven Exploration and Diversification of Social Recommendation. In 23rd International Conference on Intelligent User Interfaces (IUI '18). ACM, New York, NY, USA, 239-250. DOI: https://doi.org/10.1145/3172944.3172959 (Best Student Paper Honorable Mention Award)

5. Chun-Hua Tsai and Peter Brusilovsky. 2018. Explaining Social Recommendations to Casual Users: Design Principles and Opportunities. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion (IUI '18 Companion). ACM, New York, NY, USA, Article 59, 2 pages. DOI: https://doi.org/10.1145/3180308.3180368

6. Chun-Hua Tsai and Peter Brusilovsky. 2017. Enhancing Recommendation Diversity through a Dual Recommendation Interface. 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ‘17).

7. Chun-Hua Tsai and Peter Brusilovsky. 2017. Leveraging Interfaces to Improve Recommendation Diversity. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17), Marko Tkalcic, Dhaval Thakker, Panagiotis Germanakos, Kalina Yacef, Cecile Paris, and Olga Santos (Eds.). ACM, New York, NY, USA, 65-70. DOI: https://doi.org/10.1145/3099023.3099073

8. Chun-Hua Tsai and Peter Brusilovsky. 2017. Providing Control and Transparency in a Social Recommender System for Academic Conferences. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17). ACM, New York, NY, USA, 313-317. DOI: https://doi.org/10.1145/3079628.3079701

9. Chun-Hua Tsai. 2016. A Fuzzy-Based Personalized Recommender System for Local Businesses. In Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT '16). ACM, New York, NY, USA, 297-302. DOI: https://doi.org/10.1145/2914586.2914641

10. Chun-Hua Tsai and Yu-Ru Lin. 2016. Tracing and predicting collaboration for junior scholars. Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee.

11. Chun-Hua Tsai and Peter Brusilovsky. 2016. A personalized people recommender system using global search approach. IConference 2016 Proceedings.

12. Ryan Champagne, Julio Guerra, Chun-Hua Tsai, Jocelyn Monahan, and Rosta Farzan. 2015. Fuzziness in LGBT non-profit ICT use. In Proceedings of the Seventh International Conference on Information and Communication Technologies and Development (ICTD '15). ACM, New York, NY, USA,, Article 30 , 4 pages. DOI=http://dx.doi.org/10.1145/2737856.2737893

13. Chun-Hua Tsai and Peter Brusilovsky. 2016. A personalized people recommender system using global search approach. IConference 2016 Proceedings.

14. Chun-Hua Tsai and Yu-Ru Lin. 2014. From media reporting to international relations: a case study of Asia-pacific economic cooperation (APEC). In Proceedings of the 2014 ACM conference on Web science (WebSci '14). ACM, New York, NY, USA, 279-280. DOI: https://doi.org/10.1145/2615569.2615671