5 papers
Denoising Neural Reranker for Recommender Systems
Wenyu Mao, Shuchang Liu, Hailan Yang +9
For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recom…
On the Number of Subsequences in the Nonbinary Deletion Channel
Han Li, Xiang Wang, Fang-Wei Fu
In the deletion channel, an important problem is to determine the number of subsequences derived from a string of length when subjected to deletions. It is well-known t…
Some New Results on Sequence Reconstruction Problem for Deletion Channels
Xiang Wang, Weijun Fang, Han Li +1
Levenshtein first introduced the sequence reconstruction problem in . In the realm of combinatorics, the sequence reconstruction problem is equivalent to determining the valu…
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao +7
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models h…
Sequence Reconstruction Problem for Ternary Deletion Channels
Xiang Wang, Han Li, Fang-Wei Fu
The sequence reconstruction problem was proposed by Levenshtein in 2001. In this model, a sequence from a code is transmitted over several channels, and the decoder receives the di…