Tracking Multiple Topics for Finding Interesting Articles

We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 25% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback

Measuring the Interestingness of Articles in a Limited User Environment Prospectus

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment there are not enough users that would make collaborative filtering effective. I present a general framework for defining and measuring the ''interestingness'' of articles, called iScore, incorporating user-feedback including tracking multiple topics of interest as well as finding interesting entities or phrases in a complex relationship network. I propose and have shown the validity of the following: 1. Filtering based on only topic relevancy is insufficient for identifying interesting articles. 2. No single feature can characterize the interestingness of an article for a user. It is the combination of multiple features that yields higher quality results. For each user, these features have different degrees of usefulness for predicting interestingness. 3. Through user-feedback, a classifier can combine features to predict interestingness for the user. 4. Current evaluation corpora, such as TREC, do not capture all aspects of personalized news filtering systems necessary for system evaluation. 5. Focusing on only specific evolving user interests instead of all topics allows for more efficient resource utilization while yielding high quality recommendation results. 6. Multiple profile vectors yield significantly better results than traditional methods, such as the Rocchio algorithm, for identifying interesting articles. Additionally, the addition of tracking multiple topics as a new feature in iScore, can improve iScore's classification performance. 7. Multiple topic tracking yields better results than the best results from the last TREC adaptive filtering run. As future work, I will address the following hypothesis: Entities and the relationship among these entities using current information extraction technology can be utilized to identify entities of interest and relationships of interest, using a scheme such as PageRank. And I will address one of the following two hypotheses: 1. By addressing the multiple reading roles that a single user may have, classification results can be improved. 2. By tailoring the operating parameters of MTT, better classification results can be achieved

Academics' online presence guidelines: A four step guide to taking control of your visibility

OpenUCT published Academics' online presence guidelines: A four step guide to taking control of your visibility in 2012

Determining citizens’ opinions about stories in the news media: analysing Google, Facebook and Twitter

We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories. We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments

Topic Detection and Tracking in Personal Search History

This thesis describes a system for tracking and detecting topics in personal search history. In particular, we developed a time tracking tool that helps users in analyzing their time and discovering their activity patterns. The system allows a user to specify interesting topics to monitor with a keyword description. The system would then keep track of the log and the time spent on each document and produce a time graph to show how much time has been spent on each topic to be monitored. The system can also detect new topics and potentially recommend relevant information about them to the user. This work has been integrated with the UCAIR Toolbar, a client side agent. Considering limited resources on the client side, we designed an e????cient incremental algorithm for topic tracking and detection. Various unsupervised learning approaches have been considered to improve the accuracy in categorizing the user log into appropriate categories. Experiments show that our tool is effective in categorizing the documents into existing categories and detecting the new useful catgeories. Moreover, the quality of categorization improves over time as more and more log is available

The Reality of Measuring Human Service Programs: Results of a Survey

In the summer of 2013, Idealware created and distributed a survey to learn how human service organizations from their own mailing list are actually using technology to measure and evaluate the outcomes of their programs. The suvey looked at a general overview of outcomes measurement and program evaluation topics, from how frequently they look at data and how much time they spend doing so to what types of metrics the organizations were tracking. To further understand the realities of measuring program effectiveness, Idealware conducted a site visit and interview of three human service organizations in Portland, Maine. The results clearly show that the respondents are struggling to measure their programs