Recommender systems
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Introducing Web 3.0: HTML5 and the Semantic Web

Introducing Web 3.0: HTML5 and the Semantic Web | Recommender systems | Scoop.it

The World Wide Web started its life as a series of simple, text-based, read-only homepages whose sole purpose was to act as a digital business card and brochure. From this, Web 2.0 evolved, and this dynamic, interactive approach now informs our online life. Web 2.0 is about much more than page design; from static pages grew a community-driven, user-generated web where collaboration and information are unified.


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Web 3.0 and Semantic Search

Web 3.0 and Semantic Search | Recommender systems | Scoop.it
The transition from a web made up of websites to a web of people has began and semantic indexing is at its core.
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Building user profiles to improve user experience in recommender systems

Building user profiles to improve user experience in recommender systems | Recommender systems | Scoop.it

Recommender systems are quickly becoming ubiquitous in many Web applications, including e-commerce, social media channels, content providers, among others. These systems act as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. Crucial to the performance of a recommender system is the accuracy of the user profiles used to represent the interests of the users. In this proposal, we analyze three different aspects of user profiling: (i) selecting the most informative events from the interaction between users and the system, (ii) combining different recommendation algorithms to (iii) including trust-aware information in user profiles to improve the accuracy of recommender systems.

 

Source: http://dl.acm.org/citation.cfm?id=2433492

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ACM RecSys 2013 Workshop on Recommender Systems and the ...

The exponential growth of the social web poses challenges and new opportunities for recommender systems. The Social Web has turned information consumers into active contributors creating massive amounts of information ...
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4th International Workshop on Modeling Social Media: Mining, Modeling and Recommending 'Things' in Social Media - Workshop at Hypertext 2013

In our first workshop on Modeling Social Media (MSM 2010 in Toronto, Canada), we explored various different models of social media ranging from user modeling, hypertext models, software engineering models, sociological models and framework models. In our second workshop (MSM 2011 in Boston, USA), we addressed the user interface aspects of modeling social media. In our third workshop (MSM 2012 in Milwaukee, USA), we looked at the collective intelligence in social media, i.e. making sense of the content and context from social media websites such as Facebook, Twitter, Google+ and Foursquare by analyzing tweets, tags, blog posts, likes, posts and check-ins, in order to create a new knowledge and semantic meaning. With this year's workshop we aim to attract researchers from all over the world working in the field of social media mining, modeling and end-user applications. In particular, we would like to invite researchers working on the important field of "recommender systems" for social media which is gaining more and more in importance due to the increasing information overload problem. 

 

The goal of this workshop is to continue our vibrant discussion on social media mining and modelling with a special focus on recommender systems for social media applications. Hence, the workshop aims to attract and discuss various novel aspects of social media mining, modelling and doing recommendations on top of these data/models. In short the workshop invites topics such as social media mining methods/techniques, novel approaches to model users or things in social media, frameworks to harvest and/or display social media data and new social media recommender methods/techniques/algorithms or interfaces supporting users for instance in information finding, meta-data application etc. Thus, our goal is to bring together researchers and practitioners from all over the world with diverse backgrounds interested in 1) exploring different perspectives and approaches to mine (complex) and analyse social media data, 2) modelling social media users and 3) building applications such as recommender systems on top of this data/models.


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Ashish Umre's curator insight, January 9, 2013 8:19 AM
Submission deadlinesPaper Submission:Monday, February 04, 2013Acceptance Notification:Monday, February 18, 2013Paper Final Version Due: Thursday, February 28, 2013Workshop: May 01, 2013
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Netflix innovator - contextual algorithms will be key to future media search

Netflix innovator - contextual algorithms will be key to future media search | Recommender systems | Scoop.it

Search professionals in internet firms from Netflix to Facebook and LinkedIn are focusing on a new generation of contextual algorithms that better match users with the content they will enjoy from video to music and news, the director of product innovation at Netflix Carlos Gomez Uribe told Siliconrepublic.com.

 

Uribe was in Dublin recently at the Recommender Systems Conference where over 300 professionals working in different aspects of personalisation from organisations like Facebook and LinkedIn were gathered.

 

Not only is Netflix available across 800 connected devices, but it tries to read users’ minds fro the moment they sign in based on 20 questions that match them with genres like action or romance that match their taste.


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ScienceDirect.com - Decision Support Systems - A Social Recommender Mechanism for E-Commerce: Combining Similarity, Trust, and Relationship

Online business transactions and the success of e-commerce depend greatly on the effective design of a product recommender mechanism. This study proposes a social recommender system that can generate personalized product recommendations based on preference similarity, recommendation trust, and social relations. Compared with traditional collaborative filtering approaches, the advantage of the proposed mechanism is its comprehensive consideration of recommendation sources. Accordingly, our experimental results show that the proposed model outperforms other benchmark methodologies in terms of recommendation accuracy. The proposed framework can also be effectively applied to e-commerce retailers to promote their products and services.

 

 

Source: http://www.sciencedirect.com/science/article/pii/S0167923613000705

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