Keyword-Based TV Program Recommendation. - Christian Wartena; Wout Slakhorst; Martin Wibbels; Zeno Gantner; Christoph Freudenthaler; Chris Newell; Lars Schmidt-Thieme (ITWP@IJCAI, 2011)

Explaining the user experience of recommender systems. - Bart P. Knijnenburg; Martijn C. Willemsen; Zeno Gantner; Hakan Soncu; Chris Newell (User Model. User Adapt. Interact., 2012)

Design and evaluation of a client-side recommender system. - Chris Newell; Libby Miller (RecSys, 2013)

Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks. - Fabian Christoffel; Bibek Paudel; Chris Newell; Abraham Bernstein (RecSys, 2015)

Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications. - Bibek Paudel; Fabian Christoffel; Chris Newell; Abraham Bernstein (ACM Trans. Interact. Intell. Syst., 2017)

Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation. - Saba Nazir; Taner Cagali; Mehrnoosh Sadrzadeh; Chris Newell (ISM, 2020)

Cosine Similarity of Multimodal Content Vectors for TV Programmes. - Saba Nazir; Taner Cagali; Chris Newell; Mehrnoosh Sadrzadeh (CoRR, 2020)

Enhancing Personalised Recommendations with the Use of Multimodal Information. - Taner Cagali; Mehrnoosh Sadrzadeh; Chris Newell (ISM, 2021)

Exploring Pre-Trained Neural Audio Representations for Audio Topic Segmentation. - Iacopo Ghinassi; Matthew Purver; Huy Phan; Chris Newell (ICME, 2023)

Semantic and Lexical Token Based Vectors Improve Precision of Recommendations for TV Programmes. - Taner Cagali; Hadi Wazni; Saba Nazir; Mehrnoosh Sadrzadeh; Chris Newell (ISM, 2023)

Multimodal Topic Segmentation of Podcast Shows with Pre-trained Neural Encoders. - Iacopo Ghinassi; Lin Wang 009; Chris Newell; Matthew Purver (ICMR, 2023)

Lessons Learnt from Linear Text Segmentation: a Fair Comparison of Architectural and Sentence Encoding Strategies for Successful Segmentation. - Iacopo Ghinassi; Lin Wang 009; Chris Newell; Matthew Purver (RANLP, 2023)

The Semantic Scholar Open Data Platform. - Rodney Kinney; Chloe Anastasiades; Russell Authur; Iz Beltagy; Jonathan Bragg; Alexandra Buraczynski; Isabel Cachola; Stefan Candra; Yoganand Chandrasekhar; Arman Cohan; Miles Crawford; Doug Downey; et al. (CoRR, 2023)

Comparing neural sentence encoders for topic segmentation across domains: not your typical text similarity task. - Iacopo Ghinassi; Lin Wang 009; Chris Newell; Matthew Purver (PeerJ Comput. Sci., 2023)

When Cohesion Lies in the Embedding Space: Embedding-Based Reference-Free Metrics for Topic Segmentation. - Iacopo Ghinassi; Lin Wang 009; Chris Newell; Matthew Purver (LREC/COLING, 2024)

Recent Trends in Linear Text Segmentation: A Survey. - Iacopo Ghinassi; Lin Wang 009; Chris Newell; Matthew Purver (EMNLP (Findings), 2024)

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models. - Matt Deitke; Christopher Clark; Sangho Lee 008; Rohun Tripathi; Yue Yang 006; Jae Sung Park; Mohammadreza Salehi; Niklas Muennighoff; Kyle Lo; Luca Soldaini; Jiasen Lu; Taira Anderson; et al. (CoRR, 2024)

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models. - Matt Deitke; Christopher Clark; Sangho Lee 001; Rohun Tripathi; Yue Yang 006; Jae Sung Park; Mohammadreza Salehi; Niklas Muennighoff; Kyle Lo; Luca Soldaini; Jiasen Lu; Taira Anderson; et al. (CVPR, 2025)