# Removed blogs and profile

# Added new paper # Fixed issues with images and personality bias paper # Added more info in the projects posts # Adjusted the description to be more concise # Changed profile picture
karapostK · Jun 7, 2024 · 7f2126f · 7f2126f
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diff --git a/Gemfile.lock b/Gemfile.lock
@@ -53,8 +53,6 @@ GEM
     ffi (1.16.3)
     forwardable-extended (2.6.0)
     gemoji (4.1.0)
-    google-protobuf (4.26.1-aarch64-linux)
-      rake (>= 13)
     google-protobuf (4.26.1-x86_64-linux)
       rake (>= 13)
     html-pipeline (2.14.3)
@@ -134,7 +132,6 @@ GEM
     kramdown-parser-gfm (1.1.0)
       kramdown (~> 2.0)
     latex-decode (0.4.0)
-    libv8-node (21.7.2.0-aarch64-linux)
     libv8-node (21.7.2.0-x86_64-linux)
     liquid (4.0.4)
     listen (3.9.0)
@@ -154,8 +151,6 @@ GEM
     mutex_m (0.2.0)
     namae (1.2.0)
       racc (~> 1.7)
-    nokogiri (1.16.5-aarch64-linux)
-      racc (~> 1.4)
     nokogiri (1.16.5-x86_64-linux)
       racc (~> 1.4)
     pathutil (0.16.2)
@@ -170,9 +165,7 @@ GEM
       strscan (>= 3.0.9)
     rouge (4.2.1)
     safe_yaml (1.0.5)
-    sass-embedded (1.77.0-aarch64-linux-gnu)
-      google-protobuf (>= 3.25, < 5.0)
-    sass-embedded (1.77.0-x86_64-linux-gnu)
+    sass-embedded (1.77.2-x86_64-linux-gnu)
       google-protobuf (>= 3.25, < 5.0)
     sax-machine (1.3.2)
     strscan (3.1.0)
@@ -187,7 +180,6 @@ GEM
     webrick (1.8.1)
 
 PLATFORMS
-  aarch64-linux
   x86_64-linux-gnu
 
 DEPENDENCIES
@@ -216,4 +208,4 @@ DEPENDENCIES
   webrick
 
 BUNDLED WITH
-   2.5.7
+   2.5.10
diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib
@@ -1,6 +1,21 @@
 ---
 ---
 
+@inproceedings{melchiorre2024modular,
+    title = {Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems},
+    author = {Melchiorre, Alessandro B. and Masoudian, Shahed and Kumar, Deepak and Schedl, Markus},
+    booktitle = {Proceedings of 2024 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
+    abstract = {Recommender Systems (RSs) may inadvertently perpetuate biases based on protected attributes like gender, religion, or ethnicity. Left unaddressed, these biases can lead to unfair system behavior and privacy concerns. Interpretable RS models provide a promising avenue for understanding and mitigating such biases. In this work, we propose a novel approach to debias interpretable RS models by introducing user-specific scaling weights to the interpretable user representations of prototype-based RSs. This reduces the influence of the protected attributes on the RS's prediction while preserving recommendation utility. By decoupling the scaling weights from the original representations, users can control the degree of invariance of recommendations to their protected characteristics. Moreover, by defining distinct sets of weights for each attribute, the user can further specify which attributes the recommendations should be agnostic to. We apply our method to ProtoMF, a state-of-the-art prototype-based RS model that models users by their similarities to prototypes. We employ two debiasing strategies to learn the scaling weights and conduct experiments on ML-1M and LFM2B-DB datasets aiming at making the user representations agnostic to age and gender. The results show that our approach effectively reduces the influence of the protected attributes on the representations on both datasets, showcasing flexibility in bias mitigation, while only marginally affecting recommendation quality. Finally, we assess the effects of the debiasing weights and provide qualitative evidence, particularly focusing on movie recommendations, of genre patterns identified by ProtoMF that correlate with specific genders.},
+    year = {2024},
+    bibtex_show = {true},
+    selected = {true},
+    pdf = {melchiorre2024modular.pdf},
+    preview = {melchiorre2024modular_preview.png},
+    bibtex_show = {true},
+
+}
+
+
 @article{melchiorre2023emomtb,
     title = {Emotion-aware Music Tower Blocks (EmoMTB): An Intelligent Audiovisual Interface for Music Discovery and Recommendation},
     author = {Melchiorre, Alessandro B. and Penz, David and Ganh\"or, Christian and Lesota, Oleg and Fragoso, Vasco and Fritzl, Florian and Parada-Cabaleiro, Emilia and Schubert, Franz and Schedl, Markus},
@@ -14,7 +29,7 @@ @article{melchiorre2023emomtb
     number = {13},
     year = {2023},
     bibtex_show = {true},
-    html = {https://link.springer.com/article/10.1007/s13735-023-00275-8#citeas}
+    html = {https://link.springer.com/article/10.1007/s13735-023-00275-8}
 }
 
 @inproceedings{melchiorre2022protomf,
@@ -177,7 +192,6 @@ @K and
     pages = {533–538},
     numpages = {6},
     bibtex_show = {true},
-    selected = {true},
     pdf = {melchiorre2020personalitybias.pdf},
     preview = {melchiorre2020personalitybias_preview.png},
     slides = {melchiorre2020personalitybias_slides.pdf},

diff --git a/_config.yml b/_config.yml
@@ -191,7 +191,7 @@ announcements:
 latest_posts:
   enabled: false
   scrollable: true # adds a vertical scroll bar if there are more than 3 new posts items
-  limit: 3 # leave blank to include all the blog posts
+  limit: 5 # leave blank to include all the blog posts
 
 # -----------------------------------------------------------------------------
 # Jekyll settings
@@ -609,19 +609,19 @@ third_party_libraries:
 # Get external JSON data
 # -----------------------------------------------------------------------------
 
-jekyll_get_json:
-  - data: resume
-    json: assets/json/resume.json # it can also be an url
-jsonresume:
-  - basics
-  - work
-  - education
-  - publications
-  - projects
-  - volunteer
-  - awards
-  - certificates
-  - skills
-  - languages
-  - interests
-  - references
+#jekyll_get_json:
+#  - data: resume
+#    json: assets/json/resume.json # it can also be an url
+#jsonresume:
+#  - basics
+#  - work
+#  - education
+#  - publications
+#  - projects
+#  - volunteer
+#  - awards
+#  - certificates
+#  - skills
+#  - languages
+#  - interests
+#  - references
diff --git a/_news/24_03_8_reviewer_ecml_pkdd.md → _news/24_03_08_reviewer_ecml_pkdd.md b/_news/24_03_8_reviewer_ecml_pkdd.md → _news/24_03_08_reviewer_ecml_pkdd.md
diff --git a/_news/24_06_05_accepted_ecml_pkdd.md b/_news/24_06_05_accepted_ecml_pkdd.md
@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2024-06-05
+inline: true
+---
+
+Super happy that my research paper "Modular Debiasing of Latent User Representations in Prototype-based Recommender Systems" has been accepted at the European Conference on Machine Learning! 
diff --git a/_pages/about.md b/_pages/about.md
@@ -18,11 +18,17 @@ selected_papers: true # includes a list of papers marked as "selected={true}"
 social: true # includes social icons at the bottom of the page
 ---
 
-Hola!
+I am a PhD student in Machine Learning and Computer Science at the <a href='https://www.jku.at/en/institute-of-computational-perception/'> Institute of Computational Perception</a> at Johannes Kepler University Linz, Austria.
 
-I am a Ph.D. student at the Institute <a href='https://www.jku.at/en/institute-of-computational-perception/'> of Computational Perception</a> and at the Multimedia Mining and Search Group at the <a href='https://www.jku.at/en'>Johannes Kepler University Linz</a>, Austria. 
+My research specializes in recommender system algorithms, explainable AI, and fairness in machine learning. My work has been published in esteemed conferences and journals such as RecSys, ECML-PKDD, ECIR, AI Magazine, and Information Processing & Management.
 
-I studied Engineering in Computer Science in my Bachelor at <a href='https://www.unina.it'>Università degli Studi di Napoli Federico II</a> and in my Master at <a href='https://www.uniroma1.it'>Sapienza - Università di Roma</a> where I both graduated with full marks. 
+Additionally, I led two science communication projects showcased at the international <a href='https://karapostK.github.io/projects'> Ars Electronica Festival </a> in Linz, Austria. 
 
-My main interests revolve around the topics of **recommender system algorithms**, **explainability in AI**, and **bias & fairness**. I am particularly enthusiastic about developing interpretable models and explainability methods for recommender systems, especially in the music domain. I am also interested in investigating the relationships between users' characteristics and music preference and consumption.
-<!--- about learning new advancements in recommender systems and ---> 
+I hold both a Bachelor's and a Master's degree in Engineering in Computer Science.
+
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diff --git a/_pages/blog.md b/_pages/blog.md
diff --git a/_pages/profiles.md b/_pages/profiles.md