Session 1: Novel Applications

Date: Tuesday, Oct 7, 11:00-12:30
Moderator: Alex Tuzhilin

• LinkedIn Skills: Large-Scale Topic Extraction and Inference

  by Mathieu Bastian, Matthew Hayes, William Vaughan, Sam Shah, Peter Skomoroch, Sal Uryasev, Hyungjin Kim and Christopher Lloyd

  "Skills and Expertise" is a data-driven feature on LinkedIn, the world’s largest professional online social network, which allows members to tag themselves with topics representing their areas of expertise. In this work, we present our experiences developing this large-scale topic extraction pipeline, which includes constructing a folksonomy of skills and expertise and implementing an inference and recommender system for skills. We also discuss a consequent set of applications, such as Endorsements, which allows members to tag themselves with topics representing their areas of expertise and for their connections to provide social proof, via an “endorse” action, of that member’s competence in that topic.

• Automating Readers’ Advisory to Make Book Recommendations for K-12 Readers

  by Maria Pera and Yiu-Kai Ng

  The academic performance of students is affected by their reading ability, which explains why reading is one of the most important aspects of school curriculums. Promot- ing good reading habits among K-12 students is essential, given the enormous influence of reading on students’ de- velopment as learners and members of society. In doing so, it is indispensable to provide readers with engaging and motivating reading selections. Unfortunately, existing book recommenders have failed to offer adequate choices for K- 12 readers, since they either ignore the reading abilities of their users or cannot acquire the much-needed information to make recommendations due to privacy issues. To address these problems, we have developed Rabbit, a book recom- mender that emulates the readers’ advisory service offered at school/public libraries. Rabbit considers the readability levels of its readers and determines the facets, i.e., appeal factors, of books that evoke subconscious, emotional reac- tions on a reader. The design of Rabbit is unique, since it adopts a multi-dimensional approach to capture the reading abilities, preferences, and interests of its readers, which goes beyond the traditional book content/topical analysis. Con- ducted empirical studies have shown that Rabbit outper- forms a number of (readability-based) book recommenders.

• Exploiting Sentiment Homophily for Link Prediction

  by Guangchao Yuan, Pradeep Murukannaiah, Zhe Zhang and Munindar Singh

  Link prediction system has been extensively studied and adopted to recommendation systems on social media. With the increasing popularity of sentiment analysis on social network, knowing the relationship between users’ sentiments and link prediction is important. In this paper, we study how to exploit sentiment homophily in link prediction. We have gathered political campaign dataset of one-month on Twitter. We define a set of sentiment-based features that quantify the likelihood of two users becoming friends based on their sentiments toward topics. Our evaluation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction. Further, Adamic-Adar and Euclidean distance based measures are the best predictors. We propose a factor graph model that incorporates the sentiment-based cognitive balance theory. Our evaluation shows how our model offers help in link prediction on different kinds of graphs, compared to traditional machine learning techniques. Our work offers new insights for real-world link recommendation systems.

• A Robust Model for Paper-Reviewer Assignment

  by Xiang Liu, Torsten Suel and Nasir Memon

  Automatic expert assignment is a common problem encountered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect “good” judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding conflicts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers aspects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority information. Then a Random Walk with Restart (RWR) model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over the state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, relevance as judged by human experts.

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