Shuo Chang

I need to try this?: a statistical overview of pinterest

Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the sites social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.

Specialization, homophily, and gender in a social curation site: findings from pinterest

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)-- We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

Routing questions for collaborative answering in community question answering

Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users.We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.

Demonstration of Spectral Defragmentation in Flexible Bandwidth Optical Networking by FWM

Flexible bandwidth elastic optical networking is an attractive solution for efficiently matching allocated bandwidth with link demand, but suffers from inevitable spectral fragmentation. In this letter, we discuss spectral defragmentation in flexible bandwidth networks using four-wave mixing (FWM) and wavelength selective switch (WSS)-based wavelength conversion blocks. Simulations show a defragmentation degree of one (i.e., the number of defragmentation blocks equals one) results in 71% and 47% reductions in blocking probability under high offered load (680 Erlangs) and low offered load (220 Erlangs), respectively. Further reductions in blocking probability result from an increased defragmentation degree. Experimental results show spectral defragmentation over 500 GHz of bandwidth for a defragmentation degree of one, validating FWM- and WSS-based spectral defragmentation in flexible bandwidth networks.

Using Groups of Items for Preference Elicitation in Recommender Systems

To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items this process is an inefficient way to convert a user’s effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.

Crowd-Based Personalized Natural Language Explanations for Recommendations

Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag-based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.

AppGrouper: Knowledge-based Interactive Clustering Tool for App Search Results

A relatively new feature in Google Play Store presents mobile app search results grouped by topic, helping users to quickly navigate and explore. The underlying Search Results Clustering (SRC) system faces several challenges, including grouping search results in topical coherent clusters as well as finding the appropriate level of granularity for clustering. We present AppGrouper, an alternative approach to algorithmic-only solutions, incorporating human input in a knowledge-graph-based clustering process. AppGrouper provides an interactive interface that lets domain experts steer the clustering process in early, mid, and late stages. We deployed and evaluated AppGrouper with internal experts. We found that AppGroup improved quality of algorithm-generated app clusters on 56 out of 82 search queries. We also found that the internal experts made more changes in early and mid stages for lower-quality algorithmic results, focusing more on narrow queries. Our result suggests, in some contexts, machine learning systems can greatly benefit from steering from human experts, creating a symbiotic working relationship.

Got Many Labels?: Deriving Topic Labels from Multiple Sources for Social Media Posts using Crowdsourcing and Ensemble Learning

Online search and item recommendation systems are often based on being able to correctly label items with topical keywords. Typically, topical labelers analyze the main text associated with the item, but social media posts are often multimedia in nature and contain contents beyond the main text. Topic labeling for social media posts is therefore an important open problem for supporting effective social media search and recommendation. In this work, we present a novel solution to this problem for Google+ posts, in which we integrated a number of different entity extractors and annotators, each responsible for a part of the post (e.g. text body, embedded picture, video, or web link). To account for the varying quality of different annotator outputs, we first utilized crowdsourcing to measure the accuracy of individual entity annotators, and then used supervised machine learning to combine different entity annotators based on their relative accuracy. Evaluating using a ground truth data set, we found that our approach substantially outperforms topic labels obtained from the main text, as well as naive combinations of the individual annotators. By accurately applying topic labels according to their relevance to social media posts, the results enables better search and item recommendation.

Understanding How People Use Natural Language to Ask for Recommendations: Query Dataset

This material is based on work supported by the National Science Foundation under grants IIS-0964695, IIS-1017697, IIS-1111201, IIS- 1210863, and IIS-1218826, and by a grant from Google.