Multidimensional Social Network in the Social Recommender System

Kazienko, Przemysław; Musiał, Katarzyna; Kajdanowicz, Tomasz

doi:10.1109/tsmca.2011.2132707

Cited by 145 publications

(76 citation statements)

References 34 publications

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“…Social link information have been captured from real time social networking sites and used in devising Hybrid approaches utilizing fundamental CF methodologies [16], [28], [29], [30]. As the online content is progressively being created, edited and shared over social network communities social tagging provides a powerful way for users to organize, administer, consolidate and search for innumerable kinds of resources.…”

Section: Discussionmentioning

confidence: 99%

“…After adaptation personal weight values were analyzed directing towards the revelation that the social layer based on indirect reciprocal contact list R coc and author-opinion R ao gained in their contribution much after adaptation, by 220% and 65%, respectively, where other layers lost in their importance. The tag-based layer R t increased in average by 8% [16].…”

Section: B Social Linksmentioning

confidence: 98%

“…This led to the second phase of the evolution in recommendation system. With the rapid expansion of Web 2.0, these systems incorporated social information [8] [9], [10], [11], [12] along with information used in traditional recommendation system, leading to the development of Social based Recommendation systems [13], [14], [15], [2] [16], [17] [18]. This social information was related to the virtual social circle of the user.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

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Abstract-The issue of information overload could be effectively managed with the help of intelligent system which is capable of proactively supervising the users in accessing relevant or useful information in a tailored way, by pruning the large space of possible options. But the key challenge lies in what all information can be collected and assimilated to make effective recommendations. This paper discusses reasons for evolution of recommender systems leading to transition from traditional to social information based recommendations. Social Recommender System (SRS) exploits social contextual information in the form of social links of users, social tags, user-generated data that contain huge supplemental information about items or services that are expected to be of interest of user or about features of items. Therefore, having tremendous potential for improving recommendation quality. Systematic literature review has been done for SRS by categorizing various kinds of social-contextual information into explicit and implicit user-item information. This paper also analyses key aspects of any generic recommender system namely Domain, Personalization Levels, Privacy and Trustworthiness, Recommender algorithms to give a better understanding to researchers new in this field.

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Section: Discussionmentioning

confidence: 99%

Section: B Social Linksmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

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“…One example has been proposed by Kazienko et al [21] with a methodology to deal with heterogeneous data social networks focused on video and images, such as Flicker or Youtube. Authors create a multidimensional system with 11 layers to model the relationship between 2 users.…”

Section: Social Network Recommendationmentioning

confidence: 99%

Social and Content Hybrid Image Recommender System for Mobile Social Networks

et al. 2012

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One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

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“…Collaborative filtering is treated as a technique in assisting users to locate what they are interested in a timely manner [1]. Collaborative relationships in recommender systems can be represented as a social network [2], the growth of social networks and the development of personalized recommendation techniques have evidently improved users' experiences and delivered higher quality of services [3]. However, social recommender systems are significantly challenged by the data sparsity issue --the social network topology structure shows that only a small number of users have relatively many connections with other users, and most of the users have very few or no connections.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

Jiang¹,

Xiao²,

Liu³

2016

ijacsa

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Abstract-In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users' historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users' social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.

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Multidimensional Social Network in the Social Recommender System

Cited by 145 publications

References 34 publications

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

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