Multi-Task Learning for Entity Recommendation and Document Ranking in Web Search

2020 ◽  
Vol 11 (5) ◽  
pp. 1-24
Author(s):  
Jizhou Huang ◽  
Haifeng Wang ◽  
Wei Zhang ◽  
Ting Liu
Keyword(s):  
Web Search ◽  
Document Ranking ◽  
Task Learning

WEBYACHT: A CONCEPT-BASED SEARCH TOOL FOR WWW

1999 ◽  
Vol 08 (02) ◽  
pp. 137-156 ◽  
Author(s):  
CHING-CHI HSU ◽  
CHIA-HUI CHANG
Keyword(s):  
Information Search ◽  
Relevance Feedback ◽  
Web Search ◽  
Automatic Assessment ◽  
Feedback Mechanisms ◽  
Document Ranking ◽  
Search Results ◽  
Web Information ◽  
Search Tool ◽  
The Web

This paper describes a Web information search tool called WebYacht. The goal of WebYacht is to solve the problem of imprecise search results in current Web search engines. Due to incomplete information given by users and the diversified information published on the Web, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given as in most cases. In order to clarify the ambiguity of the short queries given by users, WebYacht adopts cluster-based browsing model as well as relevance feedback to facilitate Web information search. The idea is to have users give two to three times more feedback in the same amount of time that would be required to give feedback for conventional feedback mechanisms. With the assistance of cluster-based representation provided by WebYacht, a lot of browsing labor can be reduced. In this paper, we explain the techniques used in the design of WebYacht and compare the performances of feedback interface designs and to conventional similarity ranking search results.

scholarly journals When time meets information retrieval: Past proposals, current plans and future trends

2016 ◽  
Vol 42 (6) ◽  
pp. 725-747 ◽  
Author(s):  
Bilel Moulahi ◽  
Lynda Tamine ◽  
Sadok Ben Yahia
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Specific Information ◽  
Time Dimension ◽  
Retrieval Models ◽  
Document Ranking ◽  
Common Information ◽  
Retrieval Effectiveness ◽  
Ranking Models ◽  
Tremendous Amount

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.

Multi-task learning to rank for web search

2012 ◽  
Vol 33 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Yi Chang ◽  
Jing Bai ◽  
Ke Zhou ◽  
Gui-Rong Xue ◽  
Hongyuan Zha ◽  
...  
Keyword(s):  
Web Search ◽  
Learning To Rank ◽  
Task Learning

Multi-task learning for boosting with application to web search ranking

2010 ◽  
Author(s):  
Olivier Chapelle ◽  
Pannagadatta Shivaswamy ◽  
Srinivas Vadrevu ◽  
Kilian Weinberger ◽  
Ya Zhang ◽  
...  
Keyword(s):  
Web Search ◽  
Task Learning ◽  
Web Search Ranking

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.

Suicide Prevention Through Online Gatekeeping Using Search Advertising Techniques

Crisis ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 267-273 ◽  
Author(s):  
Hajime Sueki ◽  
Jiro Ito
Keyword(s):  
Suicidal Ideation ◽  
Suicide Prevention ◽  
Help Seeking ◽  
Service Use ◽  
Web Search ◽  
Suicide Attempts ◽  
Consultation Service ◽  
Search Advertising ◽  
Internet Users ◽  
History Of

Abstract. Background: Nurturing gatekeepers is an effective suicide prevention strategy. Internet-based methods to screen those at high risk of suicide have been developed in recent years but have not been used for online gatekeeping. Aims: A preliminary study was conducted to examine the feasibility and effects of online gatekeeping. Method: Advertisements to promote e-mail psychological consultation service use among Internet users were placed on web pages identified by searches using suicide-related keywords. We replied to all emails received between July and December 2013 and analyzed their contents. Results: A total of 139 consultation service users were analyzed. The mean age was 23.8 years (SD = 9.7), and female users accounted for 80% of the sample. Suicidal ideation was present in 74.1%, and 12.2% had a history of suicide attempts. After consultation, positive changes in mood were observed in 10.8%, 16.5% showed intentions to seek help from new supporters, and 10.1% of all 139 users actually took help-seeking actions. Conclusion: Online gatekeeping to prevent suicide by placing advertisements on web search pages to promote consultation service use among Internet users with suicidal ideation may be feasible.

A Novel Personalized Web Search with Offline Capability

2012 ◽  
Vol 3 (5) ◽  
pp. 243-245
Author(s):  
Roy T P Roy T P ◽  
◽  
Ginnu George
Keyword(s):  
Web Search
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