64 research outputs found
Information Retrieval using applied Supervised Learning for Personalized E-Commerce
Master's thesis in Computer SciencePersonalized E-Commerce Search Challenge issued by the International Conference on Information and Knowledge Management. By analyzing historical data containing browsing logs, queries, user interactions, and static data in the domain of an online retail service, we attempt to extract patterns and derive features from the data collection that will subsequently improve prediction of relevant products. A selection of supervised learning models will utilize an assembly of these features to be trained for prediction of test data. Prediction is performed on the queries given by the data collection, paired with each product item originally appearing in the query. We experiment with the possible assemblies of features along with the models and compare the results to achieve maximum prediction power. Lastly, the quality of the predictions are evaluated towards a ground truth to yield scores.submittedVersio
A Zero Attention Model for Personalized Product Search
Product search is one of the most popular methods for people to discover and
purchase products on e-commerce websites. Because personal preferences often
have an important influence on the purchase decision of each customer, it is
intuitive that personalization should be beneficial for product search engines.
While synthetic experiments from previous studies show that purchase histories
are useful for identifying the individual intent of each product search
session, the effect of personalization on product search in practice, however,
remains mostly unknown. In this paper, we formulate the problem of personalized
product search and conduct large-scale experiments with search logs sampled
from a commercial e-commerce search engine. Results from our preliminary
analysis show that the potential of personalization depends on query
characteristics, interactions between queries, and user purchase histories.
Based on these observations, we propose a Zero Attention Model for product
search that automatically determines when and how to personalize a user-query
pair via a novel attention mechanism. Empirical results on commercial product
search logs show that the proposed model not only significantly outperforms
state-of-the-art personalized product retrieval models, but also provides
important information on the potential of personalization in each product
search session
A Comparison of Supervised Learning to Match Methods for Product Search
The vocabulary gap is a core challenge in information retrieval (IR). In
e-commerce applications like product search, the vocabulary gap is reported to
be a bigger challenge than in more traditional application areas in IR, such as
news search or web search. As recent learning to match methods have made
important advances in bridging the vocabulary gap for these traditional IR
areas, we investigate their potential in the context of product search. In this
paper we provide insights into using recent learning to match methods for
product search. We compare both effectiveness and efficiency of these methods
in a product search setting and analyze their performance on two product search
datasets, with 50,000 queries each. One is an open dataset made available as
part of a community benchmark activity at CIKM 2016. The other is a proprietary
query log obtained from a European e-commerce platform. This comparison is
conducted towards a better understanding of trade-offs in choosing a preferred
model for this task. We find that (1) models that have been specifically
designed for short text matching, like MV-LSTM and DRMMTKS, are consistently
among the top three methods in all experiments; however, taking efficiency and
accuracy into account at the same time, ARC-I is the preferred model for real
world use cases; and (2) the performance from a state-of-the-art BERT-based
model is mediocre, which we attribute to the fact that the text BERT is
pre-trained on is very different from the text we have in product search. We
also provide insights into factors that can influence model behavior for
different types of query, such as the length of retrieved list, and query
complexity, and discuss the implications of our findings for e-commerce
practitioners, with respect to choosing a well performing method.Comment: 10 pages, 5 figures, Accepted at SIGIR Workshop on eCommerce 202
Neural IR Meets Graph Embedding: A Ranking Model for Product Search
Recently, neural models for information retrieval are becoming increasingly
popular. They provide effective approaches for product search due to their
competitive advantages in semantic matching. However, it is challenging to use
graph-based features, though proved very useful in IR literature, in these
neural approaches. In this paper, we leverage the recent advances in graph
embedding techniques to enable neural retrieval models to exploit
graph-structured data for automatic feature extraction. The proposed approach
can not only help to overcome the long-tail problem of click-through data, but
also incorporate external heterogeneous information to improve search results.
Extensive experiments on a real-world e-commerce dataset demonstrate
significant improvement achieved by our proposed approach over multiple strong
baselines both as an individual retrieval model and as a feature used in
learning-to-rank frameworks.Comment: A preliminary version of the work to appear in TheWebConf'19
(formerly, WWW'19
Market-Aware Models for Efficient Cross-Market Recommendation
We consider the cross-market recommendation (CMR) task, which involves
recommendation in a low-resource target market using data from a richer,
auxiliary source market. Prior work in CMR utilised meta-learning to improve
recommendation performance in target markets; meta-learning however can be
complex and resource intensive. In this paper, we propose market-aware (MA)
models, which directly model a market via market embeddings instead of
meta-learning across markets. These embeddings transform item representations
into market-specific representations. Our experiments highlight the
effectiveness and efficiency of MA models both in a pairwise setting with a
single target-source market, as well as a global model trained on all markets
in unison. In the former pairwise setting, MA models on average outperform
market-unaware models in 85% of cases on nDCG@10, while being time-efficient -
compared to meta-learning models, MA models require only 15% of the training
time. In the global setting, MA models outperform market-unaware models
consistently for some markets, while outperforming meta-learning-based methods
for all but one market. We conclude that MA models are an efficient and
effective alternative to meta-learning, especially in the global setting
Impression-Aware Recommender Systems
Novel data sources bring new opportunities to improve the quality of
recommender systems. Impressions are a novel data source containing past
recommendations (shown items) and traditional interactions. Researchers may use
impressions to refine user preferences and overcome the current limitations in
recommender systems research. The relevance and interest of impressions have
increased over the years; hence, the need for a review of relevant work on this
type of recommenders. We present a systematic literature review on recommender
systems using impressions, focusing on three fundamental angles in research:
recommenders, datasets, and evaluation methodologies. We provide three
categorizations of papers describing recommenders using impressions, present
each reviewed paper in detail, describe datasets with impressions, and analyze
the existing evaluation methodologies. Lastly, we present open questions and
future directions of interest, highlighting aspects missing in the literature
that can be addressed in future works.Comment: 34 pages, 103 references, 6 tables, 2 figures, ACM UNDER REVIE
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