Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

Personalized News Recommender using Twitter

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service Twitter . The news articles are ranked based on the popularity of the article that is identified with the help of the tweets from the Twitter\u27s public timeline. Also, user profiles are built based on the user\u27s interests and the news articles are ranked by matching the characteristics of the user profile. With the help of these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity and their relevance to the user profile

The Potential of Bookmark Based User Profiles

Driven by the explosive growth of information available online, the World-Wide-Web is currently witnessing a trend towards personalized information access. As part of this trend, numerous personalized news services are emerging. The goal of this project is to develop a prototype algorithm for using bookmarks to develop a personal profile. Ultimately, we imagine this might be used to construct a personalized RSS reader for reading news online. A reader returns a large number of news stories. To increase user satisfaction it is useful to rank them to bring the most interesting to the fore. This ranking is done by implementing a personalized profile. One way to create such a profile might be to extract it from user's bookmarks. In this paper, we describe a process for learning user interest from bookmarks and present an evaluation of its effectiveness. The goal is to utilize a user profile based on bookmarks to personalize results by filtering and re-ranking the entries returned from a set of user defined feeds

Enhanced web-based summary generation for search.

After a user types in a search query on a major search engine, they are presented with a number of search results. Each search result is made up of a title, brief text summary and a URL. It is then the user\u27s job to select documents for further review. Our research aims to improve the accuracy of users selecting relevant documents by improving the way these web pages are summarized. Improvements in accuracy will lead to time improvements and user experience improvements. We propose ReClose, a system for generating web document summaries. ReClose generates summary content through combining summarization techniques from query-biased and query-independent summary generation. Query-biased summaries generally provide query terms in context. Query-independent summaries focus on summarizing documents as a whole. Combining these summary techniques led to a 10% improvement in user decision making over Google generated summaries. Color-coded ReClose summaries provide keyword usage depth at a glance and also alert users to topic departures. Color-coding further enhanced ReClose results and led to a 20% improvement in user decision making over Google generated summaries. Many online documents include structure and multimedia of various forms such as tables, lists, forms and images. We propose to include this structure in web page summaries. We found that the expert user was insignificantly slowed in decision making while the majority of average users made decisions more quickly using summaries including structure without any decrease in decision accuracy. We additionally extended ReClose for use in summarizing large numbers of tweets in tracking flu outbreaks in social media. The resulting summaries have variable length and are effective at summarizing flu related trends. Users of the system obtained an accuracy of 0.86 labeling multi-tweet summaries. This showed that the basis of ReClose is effective outside of web documents and that variable length summaries can be more effective than fixed length. Overall the ReClose system provides unique summaries that contain more informative content than current search engines produce, highlight the results in a more meaningful way, and add structure when meaningful. The applications of ReClose extend far beyond search and have been demonstrated in summarizing pools of tweets

TSPOONS: Tracking Salience Profiles Of Online News Stories

News space is a relatively nebulous term that describes the general discourse concerning events that affect the populace. Past research has focused on qualitatively analyzing news space in an attempt to answer big questions about how the populace relates to the news and how they respond to it. We want to ask when do stories begin? What stories stand out among the noise? In order to answer the big questions about news space, we need to track the course of individual stories in the news. By analyzing the specific articles that comprise stories, we can synthesize the information gained from several stories to see a more complete picture of the discourse. The individual articles, the groups of articles that become stories, and the overall themes that connect stories together all complete the narrative about what is happening in society. TSPOONS provides a framework for analyzing news stories and answering two main questions: what were the important stories during some time frame and what were the important stories involving some topic. Drawing technical news stories from Techmeme.com, TSPOONS generates profiles of each news story, quantitatively measuring the importance, or salience, of news stories as well as quantifying the impact of these stories over time

Dynamic data placement and discovery in wide-area networks

The workloads of online services and applications such as social networks, sensor data platforms and web search engines have become increasingly global and dynamic, setting new challenges to providing users with low latency access to data. To achieve this, these services typically leverage a multi-site wide-area networked infrastructure. Data access latency in such an infrastructure depends on the network paths between users and data, which is determined by the data placement and discovery strategies. Current strategies are static, which offer low latencies upon deployment but worse performance under a dynamic workload. We propose dynamic data placement and discovery strategies for wide-area networked infrastructures, which adapt to the data access workload. We achieve this with data activity correlation (DAC), an application-agnostic approach for determining the correlations between data items based on access pattern similarities. By dynamically clustering data according to DAC, network traffic in clusters is kept local. We utilise DAC as a key component in reducing access latencies for two application scenarios, emphasising different aspects of the problem: The first scenario assumes the fixed placement of data at sites, and thus focusses on data discovery. This is the case for a global sensor discovery platform, which aims to provide low latency discovery of sensor metadata. We present a self-organising hierarchical infrastructure consisting of multiple DAC clusters, maintained with an online and distributed split-and-merge algorithm. This reduces the number of sites visited, and thus latency, during discovery for a variety of workloads. The second scenario focusses on data placement. This is the case for global online services that leverage a multi-data centre deployment to provide users with low latency access to data. We present a geo-dynamic partitioning middleware, which maintains DAC clusters with an online elastic partition algorithm. It supports the geo-aware placement of partitions across data centres according to the workload. This provides globally distributed users with low latency access to data for static and dynamic workloads.Open Acces