Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk

Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static. Moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps of 5,7,9 of (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on precision at 1, precision at 3, and precision at 5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.Comment: 6 page

Temporal models for mining, ranking and recommendation in the Web

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time

Search Engine Optimisation in UK news production

This is an Author's Accepted Manuscript of an article published in Journalism Practice, 5(4), 462 - 477, 2011, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/17512786.2010.551020.This paper represents an exploratory study into an emerging culture in UK online newsrooms—the practice of Search Engine Optimisation (SEO), which assesses its impact on news production. Comprising a short-term participant observational case study at a national online news publisher, and a series of semi-structured, in-depth interviews with SEO professionals at three further UK media organisations, the author sets out to establish how SEO is operationalised in the newsroom, and what consequences these practices have for online news production. SEO practice is found to be varied and application is not universal. Not all UK news organisations are making the most of SEO even though some publishers take a highly sophisticated approach. Efforts are constrained by time, resources and management support, as well as off-page technical issues. SEO policy is found, in some cases, to inform editorial policy, but there is resistance to the principal of SEO driving decision-making. Several themes are established which call for further research

Employment in New York City's Transit and Ground Passenger Transportation Subsector

This is one of four profiles1 developed by the New York City Labor Market Information Service (NYCLMIS) about the transportation sector's top employment subsectors. The others are about air transportation, truck transportation, and support activities for transportation. This profile is intended to help workforce development account executives with business development and job placement, career advisors with job counseling, and education and training professionals with their activities in the transit and ground passenger transportation subsector. Jobseekers can also use this information to help with career decision-making. Icons appear throughout this profile to mark findings and recommendations of special interest to these respective audiences

Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

2017 Nonprofit Communications Trends Report

This is the 7th Trends Report on nonprofit communications and after six years of research, the data on how nonprofits use various communications channels and the challenges nonprofit communicators face is solid. Most of the survey was rewrote to ask questions in the field that were largely unanswered. The survey gathered data from over 1,100 participants