29 research outputs found
Semantic Equivalence of e-Commerce Queries
Search query variation poses a challenge in e-commerce search, as equivalent
search intents can be expressed through different queries with surface-level
differences. This paper introduces a framework to recognize and leverage query
equivalence to enhance searcher and business outcomes. The proposed approach
addresses three key problems: mapping queries to vector representations of
search intent, identifying nearest neighbor queries expressing equivalent or
similar intent, and optimizing for user or business objectives. The framework
utilizes both surface similarity and behavioral similarity to determine query
equivalence. Surface similarity involves canonicalizing queries based on word
inflection, word order, compounding, and noise words. Behavioral similarity
leverages historical search behavior to generate vector representations of
query intent. An offline process is used to train a sentence similarity model,
while an online nearest neighbor approach supports processing of unseen
queries. Experimental evaluations demonstrate the effectiveness of the proposed
approach, outperforming popular sentence transformer models and achieving a
Pearson correlation of 0.85 for query similarity. The results highlight the
potential of leveraging historical behavior data and training models to
recognize and utilize query equivalence in e-commerce search, leading to
improved user experiences and business outcomes. Further advancements and
benchmark datasets are encouraged to facilitate the development of solutions
for this critical problem in the e-commerce domain.Comment: The 6th Workshop on e-Commerce and NL
'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search
With the rise of voice assistants and an increase in mobile search usage,
natural language has become an important query language. So far, most of the
current systems are not able to process these queries because of the vagueness
and ambiguity in natural language. Users have adapted their query formulation
to what they think the search engine is capable of, which adds to their
cognitive burden. With our research, we contribute to the design of interactive
search systems by investigating the genuine information need in a product
search scenario. In a crowd-sourcing experiment, we collected 132 information
needs in natural language. We examine the vagueness of the formulations and
their match to retailer-generated content and user-generated product reviews.
Our findings reveal high variance on the level of vagueness and the potential
of user reviews as a source for supporting users with rather vague search
intents
JIGGLE: Java Interactive Graph Layout Environment
JIGGLE is a Java-based platform for experimenting with numerical optimization approaches to general graph layout. It can draw graphs with undirected edges, directed edges, or a mix of both. Its features include an implementation of the Barnes-Hut tree code to quickly compute inter-node repulsion forces for large graphs and an optimization procedure based on the conjugate gradient method. JIGGLE can be accessed on the World Wide Web at http://www.cs.cmu.edu/~quixote
A Numerical Optimization Approach to General Graph Drawing
Graphs are ubiquitous, finding applications in domains ranging from software engineering to computational biology. While graph theory and graph algorithms are some of the oldest, most studied fields in computer science, the problem of visualizing graphs is comparatively young. This problem, known as graph drawing, is that of transforming combinatorial graphs into geometric drawings for the purpose of visualization. Most published algorithms for drawing general graphs model the drawing problem with a physical analogy, representing a graph as a system of springs and other physical elements and then simulating the relaxation of this physical system. Solving the graph drawing problem involves both choosing a physical model and then using numerical optimization to simulate the physical system. In thi