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Monday, 4 June 2018

Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data

Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data

ABSTRACT:
Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user’s past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.
EXISTING SYSTEM:
  • Researchers have considered tag-tag relationships for personalized QE, by selecting the most related tags from a user’s profile. However, tags might not be precise descriptions of web pages, and as a result the retrieval performance of this QE approach is somewhat disappointing. Local analysis and co-occurrence based user profile representation have also been adopted to expand the query according to a user’s interaction with the system.
  • It is worth noting that, folksonomy data are not used as a test bed as in other approaches, but rather used as an external source of information from which to extract semantic classes that are added to web search results. Moreover, terms in this approach are still based on co-occurrence statistics rather than semantic relatedness.
  • Zhou et al. proposed a personalized QE framework based on the semantic relatedness of terms inside individual user profiles. A statistical tag-topic model is created to deduce latent topics from the user’s tags and tagged documents. This model is then used to identify the most relevant terms in the user model to the user’s query and then use those terms to expand the query.
DISADVANTAGES OF EXISTING SYSTEM:
  • User profiles which contain only a user’s past annotation information may not be enough to support the effective selection of expansion terms, especially for users who have had limited previous activity with the system.
  • This may “inject” the personality of other users instead of the current user, causing problems like query shift and/or interest shift.
  • Previous personalized QE research either favors tagtag relationships or relies on the co-occurrence statistics of two terms. Given the fact that tags may not constitute precise descriptions of resources, and that methods based on pure lexical matching may miss important semantic information, the retrieval performance is generally unsatisfactory.
PROPOSED SYSTEM:
  • In this paper, we adopt a different approach to personalized QE utilizing folksonomy data. In our approach, the expansion process is based on an enriched user profile, which contains tags and annotations together with documents retrieved from an external corpus. This corpus can be viewed as a knowledge base to enhance the information stored in the user profile.
  • The whole procedure of query adaptation is hidden to the user. It happens in an implicit way based on their choices of tags and the terms used on annotated web pages. We first propose a novel model to build the enriched user profiles. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents between user annotations and documents from the external corpus. We then present two novel QE techniques.
  • The first technique approaches the problem by using topical weights-enhanced WEs to select the best possible expansion terms.
  • The second method is based on the topics learned. It calculates the topical relevance between the query and the terms inside a user profile.
ADVANTAGES OF PROPOSED SYSTEM:
  • We tackle the challenge of personalized QE utilizing folksonomy data in a novel way by integrating latent and deep semantics.
  • We propose a novel model that integrates word embeddings with topic models to construct enriched user profiles with the help of an external corpus.
  • We suggest two novel personalized QE techniques based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile.
  • The techniques demonstrate significantly better results than previously proposed non-personalized and personalized QE methods.
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS: 
  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB
SOFTWARE REQUIREMENTS: 
  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Eclipse Luna
  • Database : MYSQL
REFERENCE:
Dong Zhou, Xuan Wu, Wenyu Zhao, Séamus Lawless, and Jianxun Liu, “Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2017.


Project Cost :  10000

Thanks and Regards,
Mumbai Academics | Airoli 
8097636691 (Gaurav Sir)[Project Manager]
7506234650 (Hema Yadav)[HR]
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