309 research outputs found
Personalized Ranking in eCommerce Search
We address the problem of personalization in the context of eCommerce search.
Specifically, we develop personalization ranking features that use in-session
context to augment a generic ranker optimized for conversion and relevance. We
use a combination of latent features learned from item co-clicks in historic
sessions and content-based features that use item title and price.
Personalization in search has been discussed extensively in the existing
literature. The novelty of our work is combining and comparing content-based
and content-agnostic features and showing that they complement each other to
result in a significant improvement of the ranker. Moreover, our technique does
not require an explicit re-ranking step, does not rely on learning user
profiles from long term search behavior, and does not involve complex modeling
of query-item-user features. Our approach captures item co-click propensity
using lightweight item embeddings. We experimentally show that our technique
significantly outperforms a generic ranker in terms of Mean Reciprocal Rank
(MRR). We also provide anecdotal evidence for the semantic similarity captured
by the item embeddings on the eBay search engine.Comment: Under Revie
Creating Capsule Wardrobes from Fashion Images
We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.Comment: Accepted to CVPR 201
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
The Knowledge Tracing (KT) task plays a crucial role in personalized
learning, and its purpose is to predict student responses based on their
historical practice behavior sequence. However, the KT task suffers from data
sparsity, which makes it challenging to learn robust representations for
students with few practice records and increases the risk of model overfitting.
Therefore, in this paper, we propose a Cognition-Mode Aware Variational
Representation Learning Framework (CMVF) that can be directly applied to
existing KT methods. Our framework uses a probabilistic model to generate a
distribution for each student, accounting for uncertainty in those with limited
practice records, and estimate the student's distribution via variational
inference (VI). In addition, we also introduce a cognition-mode aware
multinomial distribution as prior knowledge that constrains the posterior
student distributions learning, so as to ensure that students with similar
cognition modes have similar distributions, avoiding overwhelming
personalization for students with few practice records. At last, extensive
experimental results confirm that CMVF can effectively aid existing KT methods
in learning more robust student representations. Our code is available at
https://github.com/zmy-9/CMVF.Comment: Accepted by ICDM 2023, 10 pages, 5 figures, 4 table
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has
been widely studied and applied on online service platforms. Existing bundle
generation methods mainly utilized user's preference from historical
interactions in common recommendation paradigm, and ignored the potential
textual query which is user's current explicit intention. There can be a
scenario in which a user proactively queries a bundle with some natural
language description, the system should be able to generate a bundle that
exactly matches the user's intention through the user's query and preferences.
In this work, we define this user-friendly scenario as Query-based Bundle
Generation task and propose a novel framework Text2Bundle that leverages both
the user's short-term interests from the query and the user's long-term
preferences from the historical interactions. Our framework consists of three
modules: (1) a query interest extractor that mines the user's fine-grained
interests from the query; (2) a unified state encoder that learns the current
bundle context state and the user's preferences based on historical interaction
and current query; and (3) a bundle generator that generates personalized and
complementary bundles using a reinforcement learning with specifically designed
rewards. We conduct extensive experiments on three real-world datasets and
demonstrate the effectiveness of our framework compared with several
state-of-the-art methods
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion
research and has attracted considerable interest in the computer vision,
multimedia, and information retrieval communities in recent years. Due to the
great demand for applications, various fashion recommendation tasks, such as
personalized fashion product recommendation, complementary (mix-and-match)
recommendation, and outfit recommendation, have been posed and explored in the
literature. The continuing research attention and advances impel us to look
back and in-depth into the field for a better understanding. In this paper, we
comprehensively review recent research efforts on fashion recommendation from a
technological perspective. We first introduce fashion recommendation at a macro
level and analyse its characteristics and differences with general
recommendation tasks. We then clearly categorize different fashion
recommendation efforts into several sub-tasks and focus on each sub-task in
terms of its problem formulation, research focus, state-of-the-art methods, and
limitations. We also summarize the datasets proposed in the literature for use
in fashion recommendation studies to give readers a brief illustration.
Finally, we discuss several promising directions for future research in this
field. Overall, this survey systematically reviews the development of fashion
recommendation research. It also discusses the current limitations and gaps
between academic research and the real needs of the fashion industry. In the
process, we offer a deep insight into how the fashion industry could benefit
from fashion recommendation technologies. the computational technologies of
fashion recommendation
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