501 research outputs found
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
Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data
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