Peer recommendation based on comments write on social networks

Social networks and virtual communities has become a popular communication tool among Internet users. Millions of users share publications about different aspects: educational, personal, cultural, etc. Therefore these social sites are rich sources of information about who can help us solve any problems. In this paper, we focus on using the written comments to recommend a person who can answer a request. An automatic analysis of information using text mining techniques was proposed to select the most suitable users. Experimental evaluations show that the proposed techniques are efficient and perform better than a standard search.Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI

Peer recommendation based on comments write on social networks

Social networks and virtual communities has become a popular communication tool among Internet users. Millions of users share publications about different aspects: educational, personal, cultural, etc. Therefore these social sites are rich sources of information about who can help us solve any problems. In this paper, we focus on using the written comments to recommend a person who can answer a request. An automatic analysis of information using text mining techniques was proposed to select the most suitable users. Experimental evaluations show that the proposed techniques are efficient and perform better than a standard search.Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line

Peer recommendation based on comments write on social networks

Social networks and virtual communities has become a popular communication tool among Internet users. Millions of users share publications about different aspects: educational, personal, cultural, etc. Therefore these social sites are rich sources of information about who can help us solve any problems. In this paper, we focus on using the written comments to recommend a person who can answer a request. An automatic analysis of information using text mining techniques was proposed to select the most suitable users. Experimental evaluations show that the proposed techniques are efficient and perform better than a standard search.Eje: XV Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras de Informática (RedUNCI

DESIGNING INFORMATION STRATEGIES FOR DIGITAL PLATFORMS: FINDINGS FROM LARGE-SCALE RANDOMIZED FIELD EXPERIMENTS

The rise of digital platforms has transformed our economy and reshaped consumer behaviors and experiences. While practitioners and researchers have a growing interest in understanding digital platforms, there is still a dearth of research on how platforms can design effective information strategies to mitigate fundamental issues such as information asymmetry and search frictions by leveraging granular data. My dissertation seeks to fill this gap. Specifically, by focusing on significant real-world problems on digital platforms, I aim to examine IT-enabled and analytics-driven information strategies and study the impact of these strategies on the users as well as on the platforms themselves. In collaboration with two different online platforms, I design and conduct three randomized field experiments to investigate the impact of informational interventions and provide actionable suggestions. In Essay 1, I examine incentive strategies for motivating effective mobile app adoptions, by comparing monetary incentives against informational incentives. I find that the usage after app adoption depends on how customers are motivated, and only information induced adoption leads to long-term increase in purchases. In Essay 2, I investigate the role of “verification” when it is made optional, and find that it serves as a very effective signaling device, especially in markets that lack other mechanisms such as reputation systems. I also find that users on the two sides of online platform use the same signal very differently, and that this is attributable to the difference in the credibility of their primary signaling-attribute of each side, viz. income in males and beauty in females. In Essay 3, I examine the effectiveness of three different recommendation systems in two-sided matching platforms with a focus on how the provisioning of potential candidates’ preference information impacts focal user’s decision-making and matching outcomes. I find that compared to “people you might prefer”, users act strategically towards “people who might prefer you” and “people who you might prefer and who might prefer you” by actively reaching out to less desirable candidates, which leads to improved outcomes. In short, the three studies present new empirical evidence of how platforms can leverage information as a tool to design effective incentives, signaling mechanisms and recommender systems to facilitate users’ decision-making, transactions and matching