Personalization of Saliency Estimation

Most existing saliency models use low-level features or task descriptions when generating attention predictions. However, the link between observer characteristics and gaze patterns is rarely investigated. We present a novel saliency prediction technique which takes viewers' identities and personal traits into consideration when modeling human attention. Instead of only computing image salience for average observers, we consider the interpersonal variation in the viewing behaviors of observers with different personal traits and backgrounds. We present an enriched derivative of the GAN network, which is able to generate personalized saliency predictions when fed with image stimuli and specific information about the observer. Our model contains a generator which generates grayscale saliency heat maps based on the image and an observer label. The generator is paired with an adversarial discriminator which learns to distinguish generated salience from ground truth salience. The discriminator also has the observer label as an input, which contributes to the personalization ability of our approach. We evaluate the performance of our personalized salience model by comparison with a benchmark model along with other un-personalized predictions, and illustrate improvements in prediction accuracy for all tested observer groups

LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents

Salience and Market-aware Skill Extraction for Job Targeting

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based \emph{salience and market-agnostic} skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present \model, our deployed \emph{salience and market-aware} skill extraction system. The proposed \model ~shows promising results in improving the online performance of job recommendation (JYMBII) ($+1.92\%$ job apply) and skill suggestions for job posters ($-37\%$ suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed \model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the \model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn.Comment: 9 pages, to appear in KDD202

Social Software, Groups, and Governance

Formal groups play an important role in the law. Informal groups largely lie outside it. Should the law be more attentive to informal groups? The paper argues that this and related questions are appearing more frequently as a number of computer technologies, which I collect under the heading social software, increase the salience of groups. In turn, that salience raises important questions about both the significance and the benefits of informal groups. The paper suggests that there may be important social benefits associated with informal groups, and that the law should move towards a framework for encouraging and recognizing them. Such a framework may be organized along three dimensions by which groups arise and sustain themselves: regulating places, things, and stories

Women's hormone levels modulate the motivational salience of facial attractiveness and sexual dimorphism

The physical attractiveness of faces is positively correlated with both behavioral and neural measures of their motivational salience. Although previous work suggests that hormone levels modulate women's perceptions of others’ facial attractiveness, studies have not yet investigated whether hormone levels also modulate the motivational salience of facial characteristics. To address this issue, we investigated the relationships between within-subject changes in women's salivary hormone levels (estradiol, progesterone, testosterone, and estradiol-to-progesterone ratio) and within-subject changes in the motivational salience of attractiveness and sexual dimorphism in male and female faces. The motivational salience of physically attractive faces in general and feminine female faces, but not masculine male faces, was greater in test sessions where women had high testosterone levels. Additionally, the reward value of sexually dimorphic faces in general and attractive female faces, but not attractive male faces, was greater in test sessions where women had high estradiol-to-progesterone ratios. These results provide the first evidence that the motivational salience of facial attractiveness and sexual dimorphism is modulated by within-woman changes in hormone levels