Friday Poster Session: Industry + Full + Short Papers

Date: Friday September 26

Industry Papers

• SPOT #1Minimize Negative Experiences in Video Recommendation Systems with Multimodal Large Language Models
  by Suman Malani (Google, Inc), Youwei Zhang (Google), Liang Liu (Google)

• SPOT #2Balanced Public Service Media Recommendation Trade-offs with a Light Carbon Footprint
  by Marcel Hauck (ARD Online and Mainz University of Applied Sciences), Michael Huber (ARD Online), Juri Diels (ARD Online), David Wittenberg (ARD Online), Dietmar Jannach (University of Klagenfurt)

• SPOT #3SASRec in Action: Real-World Adaptations for ZDF Streaming Service
  by Venkata Harshit Koneru (ZDF (Zweites Deutsches Fernsehen)), Xenija Neufeld (Accso – Accelerated Solutions GmbH), Sebastian Loth (ZDF (Zweites Deutsches Fernsehen)), Andreas Grün (ZDF (Zweites Deutsches Fernsehen))

• SPOT #4An Analysis of Learned Product Embeddings in an E-Commerce Context
  by Mate Hartstein (IKEA Retail (Ingka Group)), Eva Giannatou (IKEA Retail (Ingka Group)), Martin Tegner (IKEA Retail (Ingka Group))

• SPOT #5Efficient off-policy evaluation of content blending in station-based music experiences
  by Chelsea Weaver (Amazon Music), Arvind Balasubramanian (Amazon Music), Juan Borgnino (Amazon Music), Ben London (Amazon Music)

• SPOT #6Decoupled Entity Representation Learning for Pinterest Ads Ranking
  by Jie Liu (Pinterest, Inc), Yinrui Li (Pinterest, Inc), Jiankai Sun (Pinterest, Inc), Kungang Li (Pinterest), Han Sun (Pinterest), Sihan Wang (Pinterest, Inc), Huasen Wu (Pinterest, Inc), Siyuan Gao (Pinterest, Inc), Paulo Soares (Pinterest, Inc), Nan Li (Pinterest, Inc), Zhifang Liu (Pinterest, Inc), Haoyang Li (Pinterest, Inc), Siping Ji (Pinterest), Ling Leng (Pinterest), Prathibha Deshikachar (Pinterest)

• SPOT #7Pareto-Optimal Solution: Optimizing Engagement and Revenue
  by Shaghayegh Agah (Comcast Technology AI), Shaun Schaeffer (Comcast Technology AI), Maria Peifer (Comcast Technology AI), Neeraj Sharma (Comcast Technology AI), Ankit Maheshwari (Comcast Technology AI), Sardar Hamidian (Comcast Technology AI)

• SPOT #8NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
  by Jinfeng Xu (The University of Hong Kong), Zheyu Chen (The Hong Kong Polytechnic University), Shuo Yang (The Univerisity of Hong Kong), Jinze Li (The University of Hong Kong), Hewei Wang (Carnegie Mellon University), Wei Wang (Shenzhen MSU-BIT University), Xiping Hu (Beijing Institute of Technology), Edith Ngai (The University of Hong Kong)

Full Papers

• SPOT #9Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
  by Bereket A. Yilma (University of Luxembourg), Luis A. Leiva (University of Luxembourg)

• SPOT #10LANCE: Exploration and Reflection for LLM-based Textual Attacks on News Recommender Systems
  by Yuyue Zhao (University of Science and Technology of China), Jin Huang (University of Cambridge), Shuchang Liu (Rutgers University), Jiancan Wu (University of Science and Technology of China), Xiang Wang (University of Science and Technology of China), Maarten de Rijke (University of Amsterdam)

• SPOT #11LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation
  by Yunzhe Li (University of Illinois, Urbana-Champaign), Junting Wang (University of Illinois at Urbana-Champaign), Hari Sundaram (University of Illinois at Urbana-Champaign), Zhining Liu (University of Illinois at Urbana Champaign)

• SPOT #12An Off-Policy Learning Approach for Steering Sentence Generation towards Personalization
  by Haruka Kiyohara (Cornell University), Daniel Cao (Cornell University), Yuta Saito (Cornell University), Thorsten Joachims (Cornell University)

Short Papers

• SPOT #13A Multistakeholder Approach to Value-Driven Co-Design of Recommender Systems Evaluation Metrics in Digital Archives
  by Florian Atzenhofer-Baumgartner (Graz University of Technology), Georg Vogeler (University of Graz), Dominik Kowald (Know Center Research GmbH)

• SPOT #14Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
  by Wei-Wei Du (Sony Group Corporation), Takuma Udagawa (Sony Group Corporation), Kei Tateno (Sony Group Corporation)

• SPOT #15Beyond Visit Trajectories: Enhancing POI Recommendation via LLM-Augmented Text and Image Representations
  by Zehui Wang (University of Applied Sciences Ravensburg-Weingarten), Wolfram Höpken (University of Applied Sciences Ravensburg-Weingarten), Dietmar Jannach (University of Klagenfurt)

• SPOT #16Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
  by Matteo Attimonelli (Politecnico di Bari), Alessandro De Bellis (Politecnico di Bari), Claudio Pomo (Politecnico di Bari), Dietmar Jannach (University of Klagenfurt), Eugenio Di Sciascio (Politecnico di Bari), Tommaso Di Noia (Politecnico di Bari)

• SPOT #17Towards Personality-Aware Explanations for Music Recommendations Using Generative AI
  by Gabrielle Alves (Universidade de São Paulo), Dietmar Jannach (University of Klagenfurt), Luan Soares de Souza (Universidade de São Paulo), Marcelo Garcia Manzato (Universidade de São Paulo)

• SPOT #18Popularity-Bias Vulnerability: Semi-Supervised Label Inference Attack on Federated Recommender Systems
  by Kenji Shinoda (Toyota Motor Corporation), Takeyuki Sasai (Toyota Motor Corporation), Shintaro Fukushima (Toyota Motor Corporation)

• SPOT #19D-RDW: Diversity-Driven Random Walks for News Recommender Systems
  by Runze Li (University of Zurich), Lucien Heitz (University of Zurich), Oana Inel (University of Zurich), Abraham Bernstein (University of Zurich)

• SPOT #20Stairway to Fairness: Connecting Group and Individual Fairness
  by Theresia Veronika Rampisela (University of Copenhagen), Maria Maistro (University of Copenhagen), Tuukka Ruotsalo (University of Copenhagen), Falk Scholer (RMIT University), Christina Lioma (University of Copenhagen)

EARL Workshop Papers

From spot #21 to spot #32

INRA Workshop Papers

From spot #33 to spot #42

RecSoGood Workshop Papers

From spot #43 to spot #48

RecSys in HR Workshop Papers

From spot #49 to spot #52