Social semantic query expansion

Biancalana, Claudio; Gasparetti, Fabio; Micarelli, Alessandro; Sansonetti, Giuseppe

doi:10.1145/2508037.2508041

Cited by 40 publications

(25 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly to [3], we fix λ = 0.5 for the documents matching in equation (1). We tested four groups of configurations: baselines (without query expansion, or with the full user profile expansion), very dense, dense and sparse neighborhoods.…”

Section: Tested Configurationsmentioning

confidence: 99%

“…[1] selects terms related to the user query terms. Similarly, [4] defines a query expansion that exploits relationships between users, documents, and tags.…”

Section: Introductionmentioning

confidence: 99%

“…Query expansions tackle this problem by selecting the terms to extend user query. Our proposal benefits of both query expansion-like approaches [1,4] and social retrieval [3], and we defend the idea that social networks are beneficial to personalized retrieval by: (i) adapting the user profile using social neighbors that are constrained by the query, and (ii) selecting a part of a user profile adapted to a query.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Mulhem

Amer

Géry

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper tackles the problem of pinpointing relevant information in a social network for Personalized Information Retrieval (PIR). We start from the premise that user profiles must be filtered so that they outperform non profile based queries. The formal Profile Query Expansion Constraint is then defined. We fix a specific integration of profile and a probabilistic matching framework that fits into the constraint defined. Experiments are conducted on the Bibsonomy corpus. Our findings show that even simple profile adaptation using query is effective for Personalized Information Retrieval.

show abstract

Section: Tested Configurationsmentioning

confidence: 99%

“…[1] selects terms related to the user query terms. Similarly, [4] defines a query expansion that exploits relationships between users, documents, and tags.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Mulhem

Amer

Géry

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Episodic memory are often thought of as a"map" that ties along things in long-term memory. the 2 recollections frame the class of human user's declarative memory, and work along in user's info recollecting activities [5]. Thus, once a user's net revisitation behavior happens, s/he tends to utilize LTM, interweaved with long-term memory, to recall the antecedently centered pages.…”

Section: Introductionmentioning

confidence: 99%

Query Extension with Improved User Profiles for User tailored Search taking Advantage over Folksonomy Data

Khan¹

2018

IJRASET

View full text Add to dashboard Cite

Query expansion has been widely adopted in web search as the simplest way of endeavour the anomaly of queries. customized search utilizing folksonomy knowledge has incontestible associate extreme vocabulary pair drawback that needs even more practical question growth ways. Co-occurrence statistics, tag-tag relationships and linguistics matching approaches ar among those favored by previous analysis. However, user profiles that solely contain a user's past annotation data might not be enough to support the choice of growth terms, particularly for users with restricted previous activity with the system. we have a tendency to propose a unique model to construct enriched user profiles with the assistance of associate external corpus for customized question growth. Our model integrates the present progressive text illustration learning framework, called word embeddings, with topic models in 2 teams of pseudo-aligned documents. supported user profiles, we have a tendency to build 2 novel question growth techniques. These 2 techniques ar supported topical weights-enhanced word embeddings, and also the topical relevancy between the question and also the terms within a user profile severally. The results of associate in-depth experimental analysis, performed on 2 real-world datasets mistreatment completely different external corpora, show that our approach outperforms ancient techniques, as well as existing non-personalized and customized question growth ways. Index Terms: Personalization, Information Search and Retrieval, Query Formulation, User profiles and alert services. I. INTRODUCTIONNowadays, the net is taking part in a big role in delivering data to users' fingertips. an online page are often localized by a set universal resource locator, and displays the page content as time-varying photo. Among the common internet behaviors, internet revisitation is to re-find the antecedently viewed sites, not solely the page universal resource locator, however conjointly the page photo at that access timestamp [1]. A 6-week user study with twenty three participants showed nearly fifty eight of internet access belonged to internet revisitation [2]. Another 1-year user study involving 114 participants disclosed around four-hundredth of queries were re-finding requests [3]. in step with [4], on average, each second page loaded was already visited before by identical user, and therefore the quantitative relation of revisited pages among all visits ranges between 2 hundredth and seventy two. Psychological studies show that humans trust each long-term memory and LTM to recall data or events from the past. Human's long-term memory receives and stores temporally dated episodes or events, along with their spatial-temporal relations, whereas human's LTM, on the opposite hand, may be a structured record of facts, meanings, ideas and skills that one has nonheritable from the external world. linguistics data comes from accumulated long-term memory. Episodic memory are often thought of as a"map" that ties along things in long-ter...

show abstract

“…For example, Researchers have considered selecting the most related tags from a user's profile to expand queries (Bender et al, 2008;Bertier et al, 2009;Bouadjenek et al, 2011). Local analysis and cooccurrence based user profile representation have also been adopted to expand the query (Chirita et al, 2007;Biancalana et al, 2013). Recently, Zhou et al proposed a query expansion framework based on individual user profiles (Zhou et al, 2012a).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Personalized Search using Social Data

Zhou

Lawless

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user's interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user's usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outperformed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search.

show abstract

Social semantic query expansion

Cited by 40 publications

References 51 publications

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Axiomatic Term-Based Personalized Query Expansion Using Bookmarking System

Query Extension with Improved User Profiles for User tailored Search taking Advantage over Folksonomy Data

Enhanced Personalized Search using Social Data

Contact Info

Product

Resources

About