Query expansion based on folksonomy tag co-occurrence analysis

Jin, Song; Lin, Hongfei; Su, Sui

doi:10.1109/grc.2009.5255110

Cited by 18 publications

(5 citation statements)

References 9 publications

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“…The former includes NTN [15][16][17][18][19] and RESCAL [19][20][21][22]. The latter includes TransE [23][24][25], TransH [26][27][28][29], and TransR/CtransR [30][31][32]. The relationships are very large and very sparse in the large-scale knowledge bases; the tensor decomposition-based methods are ineffective, so it is more appropriate to select the translationbased methods [33][34][35][36].…”

Section: Knowledge Graph Representationmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Zeng

2018

Mathematical Problems in Engineering

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To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

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Section: Knowledge Graph Representationmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Zeng

2018

Mathematical Problems in Engineering

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“…Lioma et al [19] provide Social QE by considering query expansion as a logical inference and by considering the addition of tags as an extra deduction to this process. In the same spirit, Jin et al [20] propose a method in which the used expansion terms are selected from a large amount of social tags in folksonomy. A tag co-occurrence method for similar terms selection is used to choose good expansion terms from the candidate tags directly according to their potential impact on the retrieval effectiveness.…”

Section: Non-personalized Social Query Expansionmentioning

confidence: 99%

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Bouadjenek

Hacid

Bouzeghoub

2016

Information Systems

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a b s t r a c tThere is currently a number of research work performed in the area of bridging the gap between Information Retrieval (IR) and Online Social Networks (OSN). This is mainly done by enhancing the IR process with information coming from social networks, a process called Social Information Retrieval (SIR). The main question one might ask is What would be the benefits of using social information (no matter whether it is content or structure) into the information retrieval process and how is this currently done?With the growing number of efforts towards the combination of IR and social networks, it is necessary to build a clearer picture of the domain and synthesize the efforts in a structured and meaningful way. This paper reviews different efforts in this domain. It intends to provide a clear understanding of the issues as well as a clear structure of the contributions. More precisely, we propose (i) to review some of the most important contributions in this domain to understand the principles of SIR, (ii) a taxonomy to categorize these contributions, and finally, (iii) an analysis of some of these contributions and tools with respect to several criteria, which we believe are crucial to design an effective SIR approach. This paper is expected to serve researchers and practitioners as a reference to help them structuring the domain, position themselves and, ultimately, help them to propose new contributions or improve existing ones.

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“…What is the Impact of using Multi-view on Diversifying Results? In this experiment, we compare our proposed MVCQE approach with the baseline concept-based query expansion (CQE) [13] and tag-based query expansion(TQE) [3]. In TQE, tag query is expanded by its top-k related tags from Flickr that frequently co-occur with the original query.…”

Section: Study Of Mvcqe Effectivenessmentioning

confidence: 99%

Enhancing Query Expansion for Tag-based Social Image Retrieval

Ksibi¹,

Ammar²,

Amar³

2013

Proceedings of the 2nd International Workshop on Web Intelligence

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Recently, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short yielding to retrieve irrelevant images in top ranked list. To overcome this problem, an effective strategy is to produce diverse images in top ranking list covering various aspects of the query. In this context, we propose a Multi-view Concept-based Query Expansion (MVCQE) process, using a predefined list of semantic concepts and following three main steps. First, we harvest social knowledge to capture different contexts related to the query. Second, we perform a Multi-view Concepts weighting by applying concept-based query expansion for the initial query and for each of its contexts. Third, we select the most representative concepts using an adaptive threshold with respect to the dispersion of concept weights. Experiments using ambiguous queries over the NUS-WIDE dataset confirm the effectiveness of our process to improve the diversification compared to well known query expansion approaches. . .

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Query expansion based on folksonomy tag co-occurrence analysis

Cited by 18 publications

References 9 publications

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms

Enhancing Query Expansion for Tag-based Social Image Retrieval

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