Challenges in Using Peer-to-Peer Structures in Order to Design a Large-Scale Web Search Engine

2008 ◽  
pp. 461-468
Author(s):  
Hamid Mousavi ◽  
Ali Movaghar
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Peer To Peer ◽  
Web Search Engine

Reprint of: The anatomy of a large-scale hypertextual web search engine

2012 ◽  
Vol 56 (18) ◽  
pp. 3825-3833 ◽  
Author(s):  
Sergey Brin ◽  
Lawrence Page
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Web Search Engine

scholarly journals Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks

2017 ◽  
Author(s):  
Jizhou Huang ◽  
Wei Zhang ◽  
Shiqi Zhao ◽  
Shiqiang Ding ◽  
Haifeng Wang
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Training Data ◽  
Data Annotation ◽  
Web Search Engine ◽  
Clickthrough Data ◽  
Ranking Model ◽  
Knowledge Graphs ◽  
Entity Relationships

Providing a plausible explanation for the relationship between two related entities is an important task in some applications of knowledge graphs, such as in search engines. However, most existing methods require a large number of manually labeled training data, which cannot be applied in large-scale knowledge graphs due to the expensive data annotation. In addition, these methods typically rely on costly handcrafted features. In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine. Then, we build a pairwise ranking model which employs a convolutional neural network to automatically learn relevant features. The proposed model can be easily trained with backpropagation to perform the ranking task. The experiments show that our method significantly outperforms several strong baselines.

The anatomy of a large-scale hypertextual Web search engine

1998 ◽  
Vol 30 (1-7) ◽  
pp. 107-117 ◽  
Author(s):  
Sergey Brin ◽  
Lawrence Page
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Web Search Engine

scholarly journals Improving Entity Recommendation with Search Log and Multi-Task Learning

2018 ◽  
Author(s):  
Jizhou Huang ◽  
Wei Zhang ◽  
Yaming Sun ◽  
Haifeng Wang ◽  
Ting Liu
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Context Information ◽  
Context Aware ◽  
Time Step ◽  
Current Time ◽  
Task Learning ◽  
Web Search Engine ◽  
Search Logs

Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as "apple" because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements.

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