  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.
