A Collaborative Tag Recommendation Based on User Profile

Xu, De; Wang, Zhijian; Zhang, Yanli; Zong, Ping

doi:10.1109/ihmsc.2012.89

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…9. As it can be clearly seen, the recall value is dropped by average of 18.29% when compared with the recall of [37]. The recall is also compared with three different user profiles.…”

Section: Resultsmentioning

confidence: 95%

“…10. As it can be seen, it shows improvement of 8.8% when compared with [37]. To calculate F1-score we also use three different user profiles.…”

Section: Resultsmentioning

confidence: 99%

“…Fig.8shows the comparison of precisions of our approach with[37]. The experiments are performed with three different user profiles and the result shows improvement of 31.57%.…”

mentioning

confidence: 99%

“…F1-score comparisonF1-score are compared and it shows improvement too which is shown in Fig.10. As it can be seen, it shows improvement of 8.8% when compared with[37]. To calculate F1-score we also use three different user profiles.…”

mentioning

confidence: 99%

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Interaction-based Collaborative Recommendation: A Personalized Learning Environment (PLE) Perspective

Ali

Ghani²,

Latiff³

2015

KSII TIIS

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In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

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“…9. As it can be clearly seen, the recall value is dropped by average of 18.29% when compared with the recall of [37]. The recall is also compared with three different user profiles.…”

Section: Resultsmentioning

confidence: 95%

“…10. As it can be seen, it shows improvement of 8.8% when compared with [37]. To calculate F1-score we also use three different user profiles.…”

Section: Resultsmentioning

confidence: 99%

“…Fig.8shows the comparison of precisions of our approach with[37]. The experiments are performed with three different user profiles and the result shows improvement of 31.57%.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Interaction-based Collaborative Recommendation: A Personalized Learning Environment (PLE) Perspective

Ali

Ghani²,

Latiff³

2015

KSII TIIS

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“…For determining, for a given user u, a given resource r, and some n ∈ N, the set (u,r) of n recommended tags we use User profile based tag recommendation [32] Tag based user profile : profile (u) = {(w1,t1), (w2,t2)..(wn,tn)} where ti ∈ T, wi is the weight of the ti, represents the importance of this tag to the user. Items are selected using balanced strategy, item tag matrix is constructed, for user u the preference relation between ti and tj for item k is calculated and ranking is done using voting, a vector is formed by the ranking tags, the profile of user u for item k is presented, ITW -Itemtagweight matrix is constructed, user similarity using Pearson correlation coefficient is calculated and most frequently used tags from the k similar user are recommended.…”

Section: Collaborative Filtering (Cf) [14]mentioning

confidence: 99%

A Survey on Decision Support Systems in Social Media

Thangaraj¹,

Indra²

2015

IJCATR

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Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about "feeding you the information that you want, when you want it" i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.

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An Integrated Recommender System Using Semantic Web With Social Tagging System

Indra

Thangaraj

2019

International Journal on Semantic Web and Information Systems

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Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.

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A Collaborative Tag Recommendation Based on User Profile

Cited by 3 publications

References 5 publications

Interaction-based Collaborative Recommendation: A Personalized Learning Environment (PLE) Perspective

Interaction-based Collaborative Recommendation: A Personalized Learning Environment (PLE) Perspective

A Survey on Decision Support Systems in Social Media

An Integrated Recommender System Using Semantic Web With Social Tagging System

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