21-03-2013, 07:15 AM
21-03-2013, 09:26 AM
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03-05-2013, 08:45 PM
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Learn to Personalized Image Search from the Photo Sharing Websites
Learn to Personalized Image Search from the Photo Sharing Websites
04-05-2013, 09:31 AM
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22-06-2013, 12:33 PM
Learn to Personalized Image Search from the Photo Sharing Websites
[attachment=55848]
Abstract
Increasingly developed social sharing websites, like
Flickr and Youtube, allow users to create, share, annotate and
comment medias. The large-scale user-generated meta-data not
only facilitate users in sharing and organizing multimedia content,
but provide useful information to improve media retrieval
and management. Personalized search serves as one of such
examples where the web search experience is improved by
generating the returned list according to the modified user search
intents. In this paper, we exploit the social annotations and
propose a novel framework simultaneously considering the user
and query relevance to learn to personalized image search. The
basic premise is to embed the user preference and query-related
search intent into user-specific topic spaces. Since the users’
original annotation is too sparse for topic modeling, we need
to enrich users’ annotation pool before user-specific topic spaces
construction.
INTRODUCTION
Keyword-based search has been the most popular search
paradigm in today’s search market. Despite simplicity and efficiency,
the performance of keyword-based search is far from
satisfying. Investigation has indicated its poor user experience
- on Google search, for 52% of 20,000 queries, searchers did
not find any relevant results [1].
Result
Processing can be further classified into result
filtering and re-ranking. Result filtering aims to filter irrelevant
results that are not of interest to a particular user [27]. While,
result re-ranking focuses on re-ordering the results by the
degree of users’ preferences estimated. Since our work falls
into this category, we mainly review the related work on result
re-ranking. Chirita et al. [17] conducted an early work by reranking
the search results according to the cosine distance
between each URL and user interest profiles constructed. Qiu
et al. [21] extended Topic-Sensitive PageRank by incorporating
users’ preference vectors.
RELATED WORK
In recent years, extensive efforts have been focusing on
personalized search. Regarding the resources they leveraged,
explicit user profile [17], relevance feedback [18], user history
data (browsing log [19], click-through data [20], [21] and
social annotations [11], [8], [4] etc.), context information
[23] (time, location, etc.) and social network [1], [3], [16]
are exploited. For the implementation there are two primary
strategies [24], query refinement and result processing. In the
following we review the related work by the strategy they
used.
Multi-correlation Smoothness Constraints
Photo sharing websites differentiate from other social tagging
systems by its characteristic of self-tagging: most images
are only tagged by their owners. Fig.4(a) shows the #tagger statistics
for Flickr and the webpage tagging system Del.icio.us.
We can see that in Flickr, 90% images have no more than
4 taggers and the average number of tagger for each image
is about 1.9. However, the average tagger for each webpage
in Del.icio.us is 6.1. The severe sparsity problem calls for
external resources to enable information propagation.
EXPERIMENTS
A. Dataset
We perform the experiments on a large-scale web image
dataset, NUS-WIDE [33]. It contains 269,648 images with
5,018 unique tags collected from Flickr. We crawled the
images’ owner information and obtained owner user ID of
247,849 images.10. The collected images belong to 50,120
unique users. Fig.4 shows the distributions of #tagger and
#tagged images11
CONCLUSION AND FUTURE WORK
How to effectively utilize the rich user metadata in the social
sharing websites for personalized search is challenging as well
as significant. In this paper we propose a novel framework
to exploit the users’ social activities for personalized image
search, such as annotations and the participation of interest
groups. The query relevance and user preference are simultaneously
integrated into the final rank list. Experiments on a
large-scale Flickr dataset show that the proposed framework
greatly outperforms the baseline.
[attachment=55848]
Abstract
Increasingly developed social sharing websites, like
Flickr and Youtube, allow users to create, share, annotate and
comment medias. The large-scale user-generated meta-data not
only facilitate users in sharing and organizing multimedia content,
but provide useful information to improve media retrieval
and management. Personalized search serves as one of such
examples where the web search experience is improved by
generating the returned list according to the modified user search
intents. In this paper, we exploit the social annotations and
propose a novel framework simultaneously considering the user
and query relevance to learn to personalized image search. The
basic premise is to embed the user preference and query-related
search intent into user-specific topic spaces. Since the users’
original annotation is too sparse for topic modeling, we need
to enrich users’ annotation pool before user-specific topic spaces
construction.
INTRODUCTION
Keyword-based search has been the most popular search
paradigm in today’s search market. Despite simplicity and efficiency,
the performance of keyword-based search is far from
satisfying. Investigation has indicated its poor user experience
- on Google search, for 52% of 20,000 queries, searchers did
not find any relevant results [1].
Result
Processing can be further classified into result
filtering and re-ranking. Result filtering aims to filter irrelevant
results that are not of interest to a particular user [27]. While,
result re-ranking focuses on re-ordering the results by the
degree of users’ preferences estimated. Since our work falls
into this category, we mainly review the related work on result
re-ranking. Chirita et al. [17] conducted an early work by reranking
the search results according to the cosine distance
between each URL and user interest profiles constructed. Qiu
et al. [21] extended Topic-Sensitive PageRank by incorporating
users’ preference vectors.
RELATED WORK
In recent years, extensive efforts have been focusing on
personalized search. Regarding the resources they leveraged,
explicit user profile [17], relevance feedback [18], user history
data (browsing log [19], click-through data [20], [21] and
social annotations [11], [8], [4] etc.), context information
[23] (time, location, etc.) and social network [1], [3], [16]
are exploited. For the implementation there are two primary
strategies [24], query refinement and result processing. In the
following we review the related work by the strategy they
used.
Multi-correlation Smoothness Constraints
Photo sharing websites differentiate from other social tagging
systems by its characteristic of self-tagging: most images
are only tagged by their owners. Fig.4(a) shows the #tagger statistics
for Flickr and the webpage tagging system Del.icio.us.
We can see that in Flickr, 90% images have no more than
4 taggers and the average number of tagger for each image
is about 1.9. However, the average tagger for each webpage
in Del.icio.us is 6.1. The severe sparsity problem calls for
external resources to enable information propagation.
EXPERIMENTS
A. Dataset
We perform the experiments on a large-scale web image
dataset, NUS-WIDE [33]. It contains 269,648 images with
5,018 unique tags collected from Flickr. We crawled the
images’ owner information and obtained owner user ID of
247,849 images.10. The collected images belong to 50,120
unique users. Fig.4 shows the distributions of #tagger and
#tagged images11
CONCLUSION AND FUTURE WORK
How to effectively utilize the rich user metadata in the social
sharing websites for personalized search is challenging as well
as significant. In this paper we propose a novel framework
to exploit the users’ social activities for personalized image
search, such as annotations and the participation of interest
groups. The query relevance and user preference are simultaneously
integrated into the final rank list. Experiments on a
large-scale Flickr dataset show that the proposed framework
greatly outperforms the baseline.