Anticipating Information Needs Based on Check-in Activity

In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM '17), 201

Check in

An original Latin-Jazz uptempo composition. This composition uses an AABA structure. The chords are based on the jazz standard Cherokee and the melody recalls Thelonious Monk's composition Rhythm-a-ning

Electronic Ticket and Check-in System for Indico Conferences

Project Specification: This project should build on the existing participant registration module of Indico and provide additional functionalities for managing the check-in process. While in small conferences it is easy to keep track of participants with a simple paper list, such techniques become inefficient when the need to scale the process up arises. Therefore Indico’s participant registration module would be extended with the functionality to generate electronic tickets. This will allow conference organizers to keep track of attendees after they finish the registration process. As part of this project it is also necessary to develop a mobile application that will be used to scan the electronic tickets, identify the user and mark them as checked in when they arrive at the conference. Additionally Indico’s HTTP API would be extended to be used by the mobile application to retrieve data about conferences and attendees. Abstract: The main goal of this project is to simplify the check-in process for conferences that use the Indico conference management system. This is archived by extending Indico’s core to include electronic ticket generation functionality and developing a mobile application that is used to scan the electronic tickets during the check-in process. Indico’s HTTP API is also extended to provide the mobile application with the necessary data

Check-in

Check-in for the Northwestern Journal of Technology and Intellectual Property\u27s 7th Annual Symposiu

Check-in

Check-in for the Northwestern Journal of Technology and Intellectual Property\u27s 7th Annual Symposiu

Building Reusable Software Component For Optimization Check in ABAP Coding

Software component reuse is the software engineering practice of developing new software products from existing components. A reuse library or component reuse repository organizes stores and manages reusable components. This paper describes how a reusable component is created, how it reuses the function and checking if optimized code is being used in building programs and applications. Finally providing coding guidelines, standards and best practices used for creating reusable components and guidelines and best practices for making configurable and easy to use.Comment: 9 pages, 6 figure

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.Comment: Accepted by KDD 201

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1