Methods and Systems to Identify Potential Users Using Click-Based Referral of Website Visitors

This document describes systems and processes for an advertising platform. The advertising platform increases a size of a target audience (e.g., advertising list of an advertising campaign) of an advertiser by using information that is known about the visitors to the advertiser’s website. The advertising platform can enable advertisers (e.g., website administrators or other such online content providers) to expand advertising to web users that have similar interests to users that have visited the website (e.g., visitors, customers, etc.) of the advertiser. These similar users are more likely to purchase a product from the advertiser than a typical web user. The advertising platform uses click data to characterize users of an initial user list. The initial user list can be small when compared to the online marketplace as a whole. The advertising platform uses the click data of users of the initial user list (e.g., a seed list) to represent the users of the initial user list. The advertising platform uses the click data refer additional web users to the list and generate a target list. Each user of the initial user list is limited to a maximum number of referrals to the target list to maintain diversity in the type of user on the target list, such that no user’s click data model dominates the target list

Biomedical Term Recognition Using Discriminative Training

We investigate the Perceptron HMM algorithm, an instance of the averaged perceptron approach, which incorporates discriminative training into the traditional Hidden Markov Model (HMM) approach. We demonstrate the efficiency of the algorithm by applying it to the biomedical term recognition problem. We show that the Perceptron HMM overcomes the limited expressiveness of the traditional, generative HMMs by incorporating additional, potentially overlapping features. This simple and elegant learning method produces performance that is comparable to the current state-of-theart, while using only straightforward features derived from the provided training data. Our experiments illustrate the relative value of competing techniques that employ more complex learning algorithms and semantic features constructed from external resources

Biomedical Term Recognition With the Perceptron HMM Algorithm

We propose a novel approach to the identification of biomedical terms in research publications using the Perceptron HMM algorithm. Each important term is identified and classified into a biomedical concept class. Our proposed system achieves a 68.6 % F-measure based on 2,000 training Medline abstracts and 404 unseen testing Medline abstracts. The system achieves performance that is close to the state-of-the-art using only a small feature set. The Perceptron HMM algorithm provides an easy way to incorporate many potentially interdependent features.