A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Symeonidis, Panagiotis; Νανόπουλος, Αλέξανδρος; Manolopoulos, Yannis

doi:10.1109/tkde.2009.85

Cited by 143 publications

(100 citation statements)

References 36 publications

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“…This model was chosen as it performed the best in the comparative study presented in the Section 4. Details of the remaining techniques that were used in the study could be found in [2,[23][24][25][26][27][28][29][30][31]. The experiments have been performed to analyze the suitability of RTF technique for applying tag-based recommendations in e-learning environments as well as comparing graph-based and tensor based approaches for tag recommendation applied to e-learning environments.…”

Section: Applying Tag-based Recommender Systems To E-learning Environmentioning

confidence: 99%

“…Since there is no straightforward way to find the optimal values for c1, c2 and c3, we follow the technique presented by [30] that a 70% of the original diagonals of X(1), X(2) and X(3) matrices can give good approximations. Thus, c1, c2 and c3 are set to be the numbers of singular values by preserving 70% of the original diagonals of X(1), X(2) and X(3) respectively in each run.…”

Section: Higher Order Singular Value Decomposition (Hosvd) Algorithmmentioning

confidence: 99%

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Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

et al. 2017

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Personalization of the e-learning systems according to the learner's needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tagbased recommendations has been used and evaluated in a programming tutoring system.

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Section: Applying Tag-based Recommender Systems To E-learning Environmentioning

confidence: 99%

Section: Higher Order Singular Value Decomposition (Hosvd) Algorithmmentioning

confidence: 99%

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

et al. 2017

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“…Panagiotis Symeonidis et al [20] developed a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data are represented by a 3-order tensor on which latent semantic analysis and dimensionality reduction is performed using higher order singular value decomposition technique.…”

Section: Background and Related Workmentioning

confidence: 99%

Cross-domain based Event Recommendation using Tensor Factorization

Arora

Taneja

Parashar

et al. 2016

Open Computer Science

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Abstract:Context in the form of meta-data has been accredited as an important component in cross-domain collaborative filtering (CDCF). In this research paper CDCF concept is used to exploit event information (context) from two UI matrices to allow the recommendation performance of one domain (Facebook-User-Event Matrix) to benefit from the information from another domain (Bookmyshow-Event-Tag Matrix). The model based collaborative filtering approach Tensor Factorization(TF) has been used to integrate Facebook provided User-Event context information with Bookmyshow Event-Tag context information to recommend events. In contrast to the standard collaborative tag recommendation, our CDCF approach uses one User-Event matrix of Facebook that takes another Bookmyshow Event-Tag matrix as additional informant. The proposed cross-domain based Event Recommendation approach is divided into three modules-i) data collection which extracts the unstructured dataset from the two domains Bookmyshow and social networking site Facebook using API's; ii) data mapping module which is basically used to integrate the common knowledge/ data that can be shared between considered different domains (Facebook & Bookmyshow). This module integrates and reduces the data into structured events' instances. As the dataset was collected from two different sites, an intersection of both was taken out. Therefore this module is carefully designed according to reliability of information that is common between two domains; iii) 3 order tensor factorization and Latent Dirichlet Allocation (LDA) used for most preferable recommendation by less pertinent result *Corresponding Author: Anuja Arora: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: anuja.arora29@gmail.com Vaibhav Taneja: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: vaibhavtaneja2@gmail.com Sonali Parashar: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: sonali.parashar29@gmail.com Apurva Mishra: CSE/IT Department, Jaypee Institute of Information Technology, Noida, India; Email: apurvamishra95@gmail.com reduction. The proposed 3 order tensor factorization is designed for maximizing the mutual benefit from both the considered domains (organizer and user). Therefore providing three recommendations: For organizers: 1) system recommends places to conduct specific event according to maximum of attendees of a particular type of event at a specific location; 2) recommending target audience to organizer: those who are interested to attend event on the basis of past data for promotion purposes. For users: 3) recommending events to users of their interest on the basis of past record. Our result shows significant improvement in reduction of less relevant data and result effectiveness is measured through recall and precision. Reduction of less relevant recommendation is 64%, 72% and 63% for place recommendation to organizer, target audience recommendation to organizer and event recommendation to us...

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“…The collaborative labeling schemes are supposed to gather information from the public and the quality of information users can acquire will augment as the magnitude of data people supplied expands [19]. At present, the Internet's numerous collaborative labeling sites are in existence, but there is the requirement for a service to incorporate the data from the manifold sites, to build a huge and compact set of collaborative data, from which users can have more precise and richer updates than from a particular site.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

“…In this section, some of the earlier works related to collaborative labeling of web pages for easy searching and recommendation to new users are discussed briefly. Panagiotis Symeonidis et al [19] developed an integrated framework to model the three categories of entities that existed in a social tagging system: users, items, and tags. A lot of users insert metadata in the form of keywords, to interpret and classify items (songs, pictures, web links, products, etc).…”

Section: Review Of Related Workmentioning

confidence: 99%

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

Banda¹,

Bharadwaj²

2014

IJCIS

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Collaborative labeling portrays the process by which numerous users put in metadata in the form of keywords to shared data. Nowadays, collaborative labeling has grown in reputation on the web, on sites that permit users to label bookmarks, photographs and other details. It has been recently become useful and well known as one effective way of classifying items for future search, sharing information, and filtering. So, as to predict the future search of users, we propose a novel collaborative tagging-based page recommendation algorithm using fuzzy classifier. The method consists of three phases: Grouping, Rule Generation Phase and Page Recommendation Phase. In the proposed method, we calculate the resemblance of users in selecting tags and thereby, calculate the nearest neighbors of each user and cluster them. Then, the priority of tags and items for each user is calculated for constructing a Nominal Label Matrix and Nominal Page Matrix. Finally, the fuzzy rules are generated for page recommendation. The experimentation is carried out on delicious datasets and the experimental results ensured that the proposed algorithm has achieved the maximum hit ratio of 6.6% for neighborhood size of 20, which is higher than the existing technique which obtained only 5.5%.

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A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Cited by 143 publications

References 36 publications

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Cross-domain based Event Recommendation using Tensor Factorization

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

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