25-02-2013, 11:09 AM
Learn to Personalized Image Search from the Photo Sharing Websites
Learn to Personalized.pdf (Size: 119.76 KB / Downloads: 102)
ABSTRACT
Increasingly developed social sharing websites, like Flickr and Youtube,
allow users to create, share, annotate and comment Medias. The large-scale usergenerated
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.
The proposed framework contains two components:
1) A Ranking based Multi-correlation Tensor Factorization model is proposed to
perform annotation prediction, which is considered as users’ potential annotations for the
images;
2) We introduce User-specific Topic Modeling to map the query relevance and
user preference into the same user-specific topic space. For performance evaluation, two
resources involved with users’ social activities are employed. Experiments on a largescale
Flickr dataset demonstrate the effectiveness of the proposed method.
IEEE Transactions on Multimedia, VolumeP,Issue:99.
Existing System
In Existing System, Users may have different intentions for the same
query, e.g., searching for “jaguar” by a car fan has a completely different meaning from
searching by an animal specialist. One solution to address these problems is personalized
search, where user-specific information is considered to distinguish the exact intentions
of the user queries and re-rank the list results. Given the large and growing importance of
search engines, personalized search has the potential to significantly improve searching
experience.
Proposed System
In Proposed System We propose a novel personalized image search
framework by simultaneously considering user and query information. The user’s
preferences over images under certain query are estimated by how probable he/she
assigns the query-related tags to the images.
• A ranking based tensor factorization model named RMTF is proposed to predict
users’ annotations to the images.
• To better represent the query-tag relationship, we build user-specific topics and
map the queries as well as the users’ preferences onto the learned topic spaces.
User-Specific Topic Modeling
Users may have different intentions for the same query, e.g., searching for
“jaguar” by a car fan has a completely different meaning from searching by an
animal specialist. One solution to address these problems is personalized search,
where user-specific information is considered to distinguish the exact intentions
of the user queries and re-rank the list results. Given the large and growing
importance of search engines, personalized search has the potential to
significantly improve searching experience.
Personalized Image Search
In the research community of personalized search, evaluation is not an
easy task since relevance judgement can only be evaluated by the searchers
themselves. The most widely accepted approach is user study, where participants
are asked to judge the search results. Obviously this approach is very costly. In
addition, a common problem for user study is that the results are likely to be
biased as the participants know that they are being tested. Another extensively
used approach is by user query logs or click through history. However, this needs
a large-scale real search logs, which is not available for most of the researchers.
Social sharing websites provide rich resources that can be exploited for
personalized search evaluation. User’s social activities, such as rating, tagging
and commenting, indicate the user’s interest and preference in a specific
document. Recently, two types of such user feedback are utilized for personalized
search evaluation. The first approach is to use social annotations. The main
assumption behind is that the documents tagged by user with tag will be
considered relevant for the personalized query. Another evaluation approach is
proposed for personalized image search on Flickr, where the images marked
Favorite by the user u are treated as relevant when u issues queries. The two
evaluation approaches have their pros and cons and supplement for each other.
We use both in our experiments and list the results in the following.