personalized search
Recently Published Documents


TOTAL DOCUMENTS

253
(FIVE YEARS 36)

H-INDEX

24
(FIVE YEARS 2)

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Jing Yao ◽  
Zhicheng Dou ◽  
Ji-Rong Wen

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.


2021 ◽  
pp. 311-325
Author(s):  
Anas El-Ansari ◽  
Marouane Birjali ◽  
Mustapha Hankar ◽  
Abderrahim Beni-Hssane

2021 ◽  
pp. 1-17
Author(s):  
Qian Guo ◽  
Wei Chen ◽  
Huaiyu Wan

Abstract Personalized search is a promising way to improve the quality of web search, and it has attracted much attention from both academic and industrial communities. Much of the current related research is based on commercial search engine data, which can not be released publicly for such reasons as privacy protection and information security. This leads to a serious lack of accessible public datasets in this field. The few available datasets though released to the public have not become widely used in academia due to the complexity of the processing process. The lack of datasets together with the difficulties of data processing have brought obstacles to fair comparison and evaluation of personalized search models. In this paper, we constructed a large-scale dataset AOL4PS to evaluate personalized search methods, collected and processed from AOL query logs. We present the complete and detailed data processing and construction process. Specifically, to address the challenges of processing time and storage space demands brought by massive data volumes, we optimized the process of dataset construction and proposed an improved BM25 algorithm. Experiments are performed on AOL4PS with some classic and state-of-the-art personalized search methods, and the experiment results demonstrate that AOL4PS can measure the effect of personalized search models. AOL4PS is publicly available at http://github.com/wanhuaiyu/AOL4PS.


Author(s):  
Qiang Zhang ◽  
Guojun Wang ◽  
Wenjuan Tang ◽  
Karim Alinani ◽  
Qin Liu ◽  
...  

Author(s):  
Jose Triny K, Et. al.

Web pages have an increasing number of been used because thepatron interface of many software programsoftwarestructures. The simplicity of interplay with internet pages is an idealbenefit of the usage of them. However, the character interface also can get extracomplicatedwhilegreatercomplexnet pages are used to construct it. Understanding the complexity of net pages as perceived subjectively with the resource of clients is thereforecrucial to betterlayout this sort ofconsumer interface. Searching is one of thenot unusual placeassignmentachievedon the Internet. Search engines are the essentialtool of the net, from whereinyou willcollectassociatedstatistics and searched in keeping with the favoredkey-word given by the character. The recordson theinternet is developing dramatically. The consumer has to spend extra time with inside theinternetin case youneed to find outthe correctfactsthey may befascinated in. Existing net engines like Google do now no longerundergo in thoughtsuniqueneeds of character and serve eachpatron similarly. For this ambiguous query, some offiles on wonderfulsubjects are decreaselower backby engines like Google. Hence it will becomedifficult for the consumer to get the requiredcontent materialfabric. Moreover it additionally takes extra time in searching a pertinent content materialfabric. In this paper, we are able to survey the numerous algorithms for decreasing complexity in internetweb page navigations.


Author(s):  
Eunah Cho ◽  
Ziyan Jiang ◽  
Jie Hao ◽  
Zheng Chen ◽  
Saurabh Gupta ◽  
...  

Author(s):  
Xiaodan Yan ◽  
Jiwei Zhang ◽  
Haroon Elahi ◽  
Meiyi Jiang ◽  
Hui Gao

Sign in / Sign up

Export Citation Format

Share Document