Using social data as context for making recommendations

Noor, Salma; Martinez, Kirk

doi:10.1145/1552262.1552269

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…Many solutions and methods are proposed to address this issue. Common recommendation strategies are based on association rules and clustering techniques (Sobhanam, 2013), social information (Zhang et al, 2010;Noor & Martinez, 2009), ontological knowledge classification (Noor & Martinez, 2009) and hybrid user modelling approach (Wang et al, 2008).…”

Section: The Cold Start Problem -User Personas Approachmentioning

confidence: 99%

A Methodology for Optimised Cultural User Personas Experience - CURE Architecture

Konstantakis

Michalakis

Aliprantis

et al. 2018

Electronic Workshops in Computing

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@aegean Cultural Heritage Institutions (CHI) are increasingly aiming at enhancing their visitors' experiences in a personalised, immersive and engaging way. A personalised system for cultural heritage promotion potentially adapts, in terms of relevance, content and presentation according to the user's interests and needs. However, since a typical visit may be short and unrepeatable, the identification of user's profile must be quick and efficient, ensuring the successful respective personalisation process. Current paper discusses a methodology for cultural user personas extraction and identification. The CURE approach eliminates the requirement of explicit user input via registration or similar data acquisition methods and involves three main stages: data acquisition from the user's online and social activity, reasoning regarding persona similarity and finally data and experiences reuse from previous visits. Regarding the constructed personas, the proposed approach continuously adapts and refines the personas features from data gathered during multiple cultural experiences and accordingly creates, deletes or merges personas in case of significant deviation, poor correlation and convergence respectively.

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Section: The Cold Start Problem -User Personas Approachmentioning

confidence: 99%

A Methodology for Optimised Cultural User Personas Experience - CURE Architecture

Konstantakis

Michalakis

Aliprantis

et al. 2018

Electronic Workshops in Computing

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“…It was fully retired on April 20, 2012. However, Google Social Graph had a good impact on social network research community and was widely exploited in many research works (see, for instance ). There exist several similarities between Google Social Graph and SNAKE.…”

Section: Related Workmentioning

confidence: 99%

A system for extracting structural information from Social Network accounts

Buccafurri

Lax

Nocera

et al. 2014

Softw. Pract. Exper.

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SUMMARYThe social network phenomenon involves hundreds of millions of people every day. This enormous volume of activity results in a huge source of information that can be valuable in many fields, for both research and application purposes. The relevance of this information strongly depends on the evolution occurring in the social Web, in which interaction among different social networks and their cross-relationships are becoming progressively more important. This, in fact, represents the basis of an emergent scenario called Social Internetworking Scenario. However, efficiently accessing and fruitfully querying this huge information source is not easy, because no tool to support applications needing a massive utilization of cross-socialnetwork data exists. In this paper, we fill this gap by proposing Social Network Account Knowledge Extractor (SNAKE), a system supporting the extraction of structural data from a social network account. SNAKE is implemented in such a way as to be easily integrated in any social-network-based application. To show the practical relevance of our proposal, we present our experience gained in three possible real-life applications strongly relying on information provided by SNAKE.

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“…[3] have explored the possibility of utilizing declared preferences of music, TV series and movies from users' Facebook profiles for collaborative recommender systems. Noor et al [15], Abel et al [7], and Orlandi et al [6] have modelled users' profiles of interests as structured collections of weighted concepts relevant to the users by using a semantic approach. Their works are based on the analysis of text produced by users on Twitter and Facebook for extracting concepts.…”

Section: Related Workmentioning

confidence: 99%

Empowering Collaborative Intelligence by the Use of User-Centered Social Network Aggregation

Morizet‐Mahoudeaux

Abel

2013

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Online social networks (OSNs) such as Facebook, Twitter, LinkedIn, Google+, have become extremely popular and ubiquitous today. Users are actively connected to these services for creating and sharing contents and events with others, and, in some cases, this activity takes place in the scope of groups of interests. Therefore, from amongst the morass of data generated every day by users, a part of information may match the interests of certain groups. In practice, members are not all linked to each other via each OSN they are connected to. It is also not realistic to assume that each member can manually explore all others' social profiles to reach the information that may be relevant to their interests. Thus, there is a need for aggregating members' social streams on a single information support to collect relevant information, and, consequently, to promote collaborative knowledge-sharing. However, the disconnected nature of today social websites prevents a straightforward aggregation process. An efficient automated aggregation model is needed. We present, in this paper, the idea of empowering collaborative intelligence by the use of a user-centered approach for OSN aggregation. We illustrate the approach by a first experience to evaluate its impact on users information sharing and enrichment capabilities.

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Using social data as context for making recommendations

Cited by 20 publications

References 15 publications

A Methodology for Optimised Cultural User Personas Experience - CURE Architecture

A Methodology for Optimised Cultural User Personas Experience - CURE Architecture

A system for extracting structural information from Social Network accounts

Empowering Collaborative Intelligence by the Use of User-Centered Social Network Aggregation

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