Databases for quantitative history

This paper will propose that, rather than sitting on silos of data, historians that utilise quantitative methods should endeavour to make their data accessible through databases, and treat this as a new form of bibliographic entry. Of course in many instances historical data does not lend itself easily to the creation of such data sets. With this in mind some of the issues regarding normalising raw historical data will be looked at with reference to current work on nineteenth century Irish trade. These issues encompass (but are not limited to) measurement systems, geographic locations, and potential problems that may arise in attempting to unify disaggregated sources. It will discuss the need for a concerted effort by historians to define what is required from digital resources for them to be considered accurate, and to what extent the normalisation requirements for database systems may conflict with the desire for accuracy. Many of the issues that the historian may encounter engaging with databases will be common to all historians, and there would be merit in having defined standards for referencing items, such as people, places, locations, and measurements.

Improved recommendation of photo-taking locations using virtual ratings

We consider the task of collaborative recommendation of photo-taking locations. We use datasets of geotagged photos. We map their locations to a location grid using a geohashing algorithm, resulting in a user x location implicit feedback matrix. Our improvements relative to previous work are twofold. First, we create virtual ratings by spreading users' preferences to neighbouring grid locations. This makes the assumption that users have some preference for locations close to the ones in which they take their photos. These virtual ratings help overcome the discrete nature of the geohashing. Second, we normalize the implicit frequency-based ratings to a 1-5 scale using a method that has been found to be useful in music recommendation algorithms. We demonstrate the advantages of our approach with new experiments that show large increases in hit rate and related metrics.