Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Supervised learning for image search result diversification
Download
index.pdf
Date
2019
Author
Göynük, Burak
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
215
views
105
downloads
Cite This
Due to ambiguity of user queries and growing size of data living on the internet, methods for diversifying search results have gained more importance lately. While earlier works mostly focus on text search, a similar need also exists for image data, which grows rapidly as people produce and share image data via their smartphones and social media applications such as Instagram, Snapchat, and Facebook. Therefore, in this thesis, we focus on the result diversification problem for image search. To this end, as our first contribution, we adopt R-LTR, a supervised learning approach that has been proposed for textual data and modify it to allow tuning the weights of visual and textual features separately, as would be required for better diversification. As a second contribution, we extend R-LTR by applying an alternative paradigm that takes into account an upperbound for the future diversity contribution that can be provided by the result being scored. We implement R-LTR and its variants using PyTorch’s neural network framework, which enables us to go beyond the original linear formulation. Finally, we create an ensemble of the most promising approaches for the image diversification problem. Our experiments using a benchmark dataset with 153 queries and 45K images reveal that the adopted supervised algorithm, RLTR, significantly outperforms various ad hoc diversification approaches in terms of thesub-topicrecallmetric. Furthermore,certainvariantsofR-LTRproposedhereare superior to the original method and provide additional (relative) gains of up to 2.2%
Subject Keywords
Search engines.
,
Keywords: information retrieval
,
search result diversification
,
image diversification
,
supervised learning
,
tensor.
URI
http://etd.lib.metu.edu.tr/upload/12624857/index.pdf
https://hdl.handle.net/11511/45127
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Advanced methods for diversification of results in general-purpose and specialized search engines
Yiğit Sert, Sevgi; Altıngövde, İsmail Sengör; Ulusoy, Özgür; Department of Computer Engineering (2020-12-28)
Diversifying search results is a common mechanism in information retrieval to satisfy more users by surfacing documents that address different possible intentions of users. It aims to generate a result list that is both relevant and diverse when ambiguous and/or broad queries appear. Such queries have different underlying subtopics (a.k.a., aspects or interpretations) that search result diversification algorithms should consider. In this thesis, we first address search result diversification as a useful met...
Limitations and improvement opportunities for implicit result diversification in search engines
Ulu, Yaşar Barış; Altıngövde, İsmail Sengör; Department of Computer Engineering (2019)
Search engine users essentially expect to find the relevant results for their query. Additionally, the results of the query should contain different possible query intents, which leads to the well-known problem of search result diversification. Our work first investigates the limitations of implicit search result diversification, and in particular, reveals that typical optimization tricks (such as clustering) may not necessarily improve the diversification effectiveness. Then, as our second contribution, we...
Explicit Search Result Diversification Using Score and Rank Aggregation Methods
Ozdemiray, Ahmet Murat; Altıngövde, İsmail Sengör (2015-06-01)
Search result diversification is one of the key techniques to cope with the ambiguous and underspecified information needs of web users. In the last few years, strategies that are based on the explicit knowledge of query aspects emerged as highly effective ways of diversifying search results. Our contributions in this article are two-fold. First, we extensively evaluate the performance of a state-of-the-art explicit diversification strategy and pin-point its potential weaknesses. We propose basic yet novel ...
Characterizing web search queries that match very few or no results
Altıngövde, İsmail Sengör; Cambazoglu, Berkant Barla; Ozcan, Rifat; Sarigil, Erdem; Ulusoy, Özgür (2012-12-19)
Despite the continuous efforts to improve the web search quality, a non-negligible fraction of user queries end up with very few or even no matching results in leading web search engines. In this work, we provide a detailed characterization of such queries based on an analysis of a real-life query log. Our experimental setup allows us to characterize the queries with few/no results and compare the mechanisms employed by the major search engines in handling them.
Effective & efficient methods for web search result diversification
Özdemiray, Ahmet Murat; Altıngövde, İsmail Sengör; Department of Computer Engineering (2015)
Search result diversification is one of the key techniques to cope with the ambiguous and/or underspecified information needs of the web users. In this study we first extensively evaluate the performance of a state-of-the-art explicit diversification strategy and pin-point its weaknesses. We propose basic yet novel optimizations to remedy these weaknesses and boost the performance of this algorithm. Secondly, we cast the diversification problem to the problem of ranking aggregation and propose to materializ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Göynük, “Supervised learning for image search result diversification,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.