Query and Product Suggestion for Price Comparison Search Engines based on Query-product Click-through Bipartite Graphs

Noce, Lucia; Gallo, Ignazio; Zamberletti, Alessandro

doi:10.5220/0005753400170024

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ma et al [13] applied a union matrix which combines query-URL bipartite graph and user-query bipartite graph to learn low-dimensional latent feature vectors of queries and proposed a solution for calculating query similarity using those feature vectors. The query-product clickthrough bipartite graph [14] was proposed by search engine logs and specific domain features such as categories and products popularities. In those approaches above, mining URL information and features can gain query relevance.…”

Section: Related Workmentioning

confidence: 99%

Query Recommendation Using Hybrid Query Relevance

2018

Future Internet

View full text Add to dashboard Cite

With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Query Recommendation Using Hybrid Query Relevance

2018

Future Internet

View full text Add to dashboard Cite

show abstract

“…For several applications, classifying documents into their respective classes is a prerequisite step. If documents are well-sorted, it can be dispatched to the relative department for processing [5]. The indexing efficiency of a digital library can be improved with document classification [6].…”

Section: Introductionmentioning

confidence: 99%

Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Nasir

Khan

Yasmin

et al. 2020

Sensors

View full text Add to dashboard Cite

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

show abstract

Personalized, Sequential, Attentive, Metric-Aware Product Search

Pan

Liang

Ren

et al. 2021

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

show abstract

Query and Product Suggestion for Price Comparison Search Engines based on Query-product Click-through Bipartite Graphs

Cited by 3 publications

References 16 publications

Query Recommendation Using Hybrid Query Relevance

Query Recommendation Using Hybrid Query Relevance

Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Personalized, Sequential, Attentive, Metric-Aware Product Search

Contact Info

Product

Resources

About