Reading News with a Purpose: Explaining User Profiles for Self-Actualization

πŸ“… April 10, 2019 β€’ πŸ• 11:15 β€’ 🏷 Papers β€’ πŸ‘ 157

Really excited to have co-authored “Reading News with a Purpose,” which was accepted at the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data (ExHUM), at UMAP 2019!

With the largest list of authors (ranging from philosophers via polcomm researchers to computer scientists), from a wide array of institutions; Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Darcia Wilkinson, and Nava Tintarev!

This is work that came out of our ICT with Industry project “Opening the black box of user profiles in content-based recommender systems” where we (FD Mediagroep) collaborated with Nava Tintarev and our excellent team of academics in a week-long academic hackathon!

Read the pre-print, below:

[bibtex file=citations.bib key=sullivan2019reading]

Read the original idea that sparked the project, presented at the 2nd FATREC Workshop at RecSys 2018, here:

[bibtex file=citations.bib key=graus2018let]

Demo Paper: “SMART Radio: Personalized News Radio”

πŸ“… November 21, 2018 β€’ πŸ• 11:31 β€’ 🏷 Papers β€’ πŸ‘ 37

We’re demo’ing SMART Radio at The 17th Dutch-Belgian Information Retrieval workshop (DIR 2018). We wrote a short paper titled “SMART Radio: Personalized News Radio” to accompany the demo, read it by clicking below!

[bibtex file=citations.bib key=sappelli2018smartradio]

Position paper “β€œLet Me Tell You Who You are” β€” Explaining Recommender Systems by Opening Black Box User Profiles”

πŸ“… November 11, 2018 β€’ πŸ• 11:21 β€’ 🏷 Papers β€’ πŸ‘ 39

Our position paper “β€œLet Me Tell You Who You are” β€” Explaining Recommender Systems by Opening Black Box User Profiles” was accepted at the 2nd FATREC Workshop on Responsible Recommendation, held at RecSys ’18!

In this paper, we detail some our ideas and approaches of providing transparency in recommendations through displaying the user profiles, used ‘internally’ by our recommender system. Read the pre-print below!

[bibtex file=citations.bib key=graus2018let]

FATREC Position paper: Explaining recommender systems by opening black box user profiles

Pre-print of position paper “SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News”

πŸ“… June 1, 2018 β€’ πŸ• 13:44 β€’ 🏷 Papers β€’ πŸ‘ 146

Our position paperΒ “SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News” was accepted atΒ Algorithmic Personalization and News (APEN18) workshop, held at ICWSM ’18!

In this paper, we detail some of the ideas and opportunities of personalization in the domain of financial economic news. Read the pre-print below!

[bibtex file=citations.bib key=sappelli2018smart]

“The birth of collective memories” published in JASIST!

πŸ“… February 5, 2018 β€’ πŸ• 08:06 β€’ 🏷 Papers and Research β€’ πŸ‘ 67

The journal paper “The birth of collective memories: Analyzing emerging entities in text streams” I wrote with Daan Odijk and Maarten de Rijke is now (finally) published at JASIST! It is published under OpenAccess/CC BY 4.0 and available in “early view” (published before it’s published) in the Wiley Online Library. Click on the image below to access it:

The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams

πŸ“… December 11, 2017 β€’ πŸ• 16:15 β€’ 🏷 Papers β€’ πŸ‘ 170

Our paper “The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams” was accepted for publication at JASIST (the Journal of the Association for Information Science and Technology)! Grab a pre-print here:

[bibtex file=citations.bib key=graus2018birth]

This paper is is:
1. My first journal paper
2. Based on Chapter 3 of my PhD thesis “Entities of Interest — Discovery in Digital Traces
3. The first collabo on a paper (on paper) between the FD Mediagroep, Blendle, and the UvA
4. The tombstone on my academic career! (?)

In this paper we study news and social media streams spanning over 18 months, and comprising over 579 million documents, and analyze ’emergence patterns’ of entities, i.e., how a real-world entity (such as a person, organization, product, etc.) appears in these documents in the timespan between the entity’s first mention in online text streams, and when an article devoted to the entity is subsequently added to Wikipedia.