Semantic inference of user’s reputation and expertise to improve collaborative recommendations

Martín-Vicente, Manuela I.; Gil‐Solla, Alberto; Ramos‐Cabrer, Manuel; Blanco‐Fernández, Yolanda; López‐Nores, Martín

doi:10.1016/j.eswa.2012.01.162

Cited by 24 publications

(8 citation statements)

References 18 publications

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“…This subclass primarily considered three types of approaches. First, some articles focused on the user, trying to extract and process data on personality (Capuano et al 2015), product-specific knowledge and interior desire (Chou et al 2010), reputation and expertise (Martín-Vicente et al 2012), or implicit needs ). Second, information on product views and clickstream behavior was applied for user identification (Yang 2010), psychographic segmentation (Hong & Kim 2012), customer life-cycle stage assessment, and the enhancement of collaborative filtering (Ahn, Kang & Lee 2010).…”

Section: "Insertmentioning

confidence: 99%

Web personalization: The state of the art and future avenues for research and practice

Salonen

Karjaluoto

2016

Telematics and Informatics

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Section: "Insertmentioning

confidence: 99%

Web personalization: The state of the art and future avenues for research and practice

Salonen

Karjaluoto

2016

Telematics and Informatics

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“…As a result, it may not recommend new experts. The works by Martín-Vicente et al [14] and Massa et al [15] consider building a trust network for recommendation either by inferred action, i.e., approving similar items, as in [14], or by explicit trust action as in [15]. In our work, the trust network is realized by the explicit social (acquaintances) and collaboration links.…”

Section: Related Workmentioning

confidence: 95%

Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects

Allaho

Lee

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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We consider the experts recommendation problem for open collaborative projects in large-scale Open Source Software (OSS) communities. In large-scale online community, recommending expert collaborators to a project coordinator or lead developer has two prominent challenges: (i) the "cold shoulder" problem, which is the lack of interest from the experts to collaborate and share their skills, and (ii) the "cold start" problem, which is an issue with community members who has scarce data history. In this paper, we consider the Degree of Knowledge (DoK) which imposes the knowledge of the skills factor, and the Social Relative Importance (SRI) which imposes the social distance factor to tackle the aforementioned challenges. We propose four DoK models and integrate them with three SRI methods under our proposed Expert Ranking (ER) framework to rank the candidate expert collaborators based on their likelihood of collaborating in response to a query formulated by the social network of a query initiator and certain required skills to a project/task. We evaluate our proposal using a dataset collected from Github.com, which is one of the most fast-growing, largescale online OSS community. In addition, we test the models under different data scarcity levels. The experiment shows promising results of recommending expert collaborators who tend to make real collaborations to projects.

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“…Several other works have developed proposals for trust inference, which are related to the second approach. O'Donovan and Smyth [14] extend the traditional user × item space to a user × item × context space, defining a trust matrix inferred from user ratings; a similar proposal was developed by Martín-Vicente et al [15] but for expertise and reputation inferences. Other proposals go beyond and try to infer trust from social network information.…”

Section: Related Workmentioning

confidence: 99%

Recommending Ads from Trustworthy Relationships in Pervasive Environments

Martínez-Pabón

Ospina-Quintero²,

Ramírez-González

et al. 2016

Mobile Information Systems

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The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users' cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual information to be more effective. The inclusion of users' social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads' influence from other users in their closest social circle. However, there is no consensus about the variables to use during the trust inference process, and its integration into a classic collaborative filtering recommender system deserves a deeper research. On the other hand, the pervasive advertising domain demands a recommender system evaluation from a novelty/precision perspective. The improvement of the precision/novelty balance is not only a matter related to the recommendation algorithm itself but also a better recommendations' display strategy. In this paper, we propose a novel approach for a collaborative filtering recommender system based on trust, which was tested throughout a digital signage prototype using a multiscreen scheme for recommendations delivery to evaluate our proposal using a novelty/precision approach.

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Semantic inference of user’s reputation and expertise to improve collaborative recommendations

Cited by 24 publications

References 18 publications

Web personalization: The state of the art and future avenues for research and practice

Web personalization: The state of the art and future avenues for research and practice

Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects

Recommending Ads from Trustworthy Relationships in Pervasive Environments

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