Surfacing a ranked list of items for a search query to help buyers discover inventory and make purchase decisions is a critical problem in eCommerce search. Typically, items are independently predicted with a probability of sale with respect to a given search query. But in a dynamic marketplace like eBay, even for a single product, there are various different factors distinguishing one item from another which can influence the purchase decision for the user. Users have to make a purchase decision by considering all of these options. Majority of the existing learning to rank algorithms model the relative relevance between labeled items only at the loss functions like pairwise or list-wise losses. But they are limited to point-wise scoring functions where items are ranked independently based on the features of the item itself. In this paper, we study the influence of an item's neighborhood to its purchase decision. Here, we consider the neighborhood as the items ranked above and below the current item in search results. By adding delta features comparing items within a neighborhood and learning a ranking model, we are able to experimentally show that the new ranker with delta features outperforms our baseline ranker in terms of Mean Reciprocal Rank (MRR). The ranking models with proposed delta features result in $3-5\%$ improvement in MRR over the baseline model. We also study impact of different sizes for neighborhood. Experimental results show that neighborhood size $3$ perform the best based on MRR with an improvement of $4-5\%$ over the baseline model.
Frequency-aware Graph Signal Processing for Collaborative Filtering
Fudan University（复旦大学）；Shanghai Key Laboratory of Data Science, Fudan University（复旦大学上海市数据科学重点实验室）；Microsoft Research Asia（微软亚洲研究院）；Shanghai Institute of Intelligent Electronics & Systems, Fudan University（复旦大学上海智能电子与系统研究院）
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
School of Information, Renmin University of China（中国人民大学信息学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京市大数据管理与分析方法重点实验室）
GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
Harbin Institute of Technology Qingdao Research Institute（哈尔滨工业大学（青岛）研究院）；Harbin Institute of Technology (Weihai)（哈尔滨工业大学（威海））；Shandong Provincial Key Laboratory of Digital Service Computing Technology and Systems（山东省数字服务计算技术系统重点实验室）
