961 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of Texas at AustinUS8 papers
- Cornell UniversityUS6 papers
- University of BristolGB6 papers
- Princeton UniversityUS5 papers
- University of California, BerkeleyUS5 papers
- Google (United States)US4 papers
- University of Southern CaliforniaUS4 papers
- Columbia UniversityUS3 papers
- Massachusetts Institute of TechnologyUS3 papers
- Pennsylvania State UniversityUS3 papers
- Purdue University West LafayetteUS3 papers
- Stanford UniversityUS3 papers
11 papers · 1 filter
GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Lequn Wang, Jiangwei Pan, Linas Baltrunas +1
We present GenPage, an end-to-end generative approach to Netflix homepage construction that replaces the traditional multi-stage recommender stack with a single transformer. GenPag…
Towards Generalizable and Efficient Large-Scale Generative Recommenders
Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya
Generative recommendation models can model user behavior as sequences of events and provide a shared backbone for multiple recommendation tasks. In production, however, pre-trainin…
Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang +2
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic…
Orthogonal Low Rank Embedding Stabilization
Kevin Zielnicki, Ko-Jen Hsiao
The instability of embedding spaces across model retraining cycles presents significant challenges to downstream applications using user or item embeddings derived from recommendat…
Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
Swanand Joshi, Yesu Feng, Ko-Jen Hsiao +2
Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys…
Is Cosine-Similarity of Embeddings Really About Similarity?
Harald Steck, Chaitanya Ekanadham, Nathan Kallus
Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations. A popular application is to quantify semantic simila…
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
Learning Representations of Hierarchical Slates in Collaborative Filtering
Ehtsham Elahi, Ashok Chandrashekar
We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchic…
Markov Random Fields for Collaborative Filtering
Harald Steck
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon…
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show tha…
Challenges in Search on Streaming Services: Netflix Case Study
Sudarshan Lamkhede, Sudeep Das
We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search…