QUERY EXPANSION WITH ENRICHED USER PROFILES FOR PERSONALIZED SEARCH UTILIZING FOLKSONOMY DATA
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The document proposes two novel techniques for personalized query expansion using folksonomy data. It introduces a model that constructs enriched user profiles by integrating word embeddings with topic models from user annotations and an external corpus. The first technique selects expansion terms using topical weights-enhanced word embeddings. The second calculates topical relevance between the query and user profile terms. An evaluation shows the approaches outperform existing non-personalized and personalized query expansion methods.
QUERY EXPANSION WITH ENRICHED USER PROFILES FOR PERSONALIZED SEARCH UTILIZING FOLKSONOMY DATA
- 1. 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 Over the past number of years personalized search algorithms which utilize folksonomy data have attracted significant attention in the literature . This is partially due to the relative unavailability of users’ search and click-through history to independent researchers not employed by, or engaged with, a commercial search engine. Another reason for utilizing folksonomy data is that tags are highly ambiguous, representing a typical realworld Web search scenario of short queries formulated by users. “Folksonomy” is a term typically used to describe the social classification phenomenon. Online folksonomy services are used by millions of users
- 2. world-wide, enabling users to save and organize their online bookmarks with freely chosen short text descriptors. DISADVANTAGES: 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. Previous personalized QE research either favors tagtag relationships or relies on the co- occurrence statistics of two terms. 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. ADVANTAGES 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. Based on these enhanced user profiles, we then present two novel QE techniques. The first technique approaches the problem by using topical weights-enhanced word embeddings to select the best possible expansion terms. The second technique calculates the topical relevance between the query and the terms inside a user profile.
- 3. OBJECTIVES 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. Architecture Diagram SYSTEM REQUIREMENTS
- 4. H/W SYSTEM CONFIGURATION:- Processor - Pentium –IV Speed - 1.5 Ghz RAM - 512 MB(min) Hard Disk - 40 GB S/W SYSTEM CONFIGURATION Operating System :Windows95/98/2000/XP Application Server : Tomcat5.0/6.X Front End : HTML, Java, Jsp Scripts : JavaScript. Server side Script : Java Server Pages. Database Connectivity : Mysql.