4,094 research outputs found
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Semantic Tagging on Historical Maps
Tags assigned by users to shared content can be ambiguous. As a possible
solution, we propose semantic tagging as a collaborative process in which a
user selects and associates Web resources drawn from a knowledge context. We
applied this general technique in the specific context of online historical
maps and allowed users to annotate and tag them. To study the effects of
semantic tagging on tag production, the types and categories of obtained tags,
and user task load, we conducted an in-lab within-subject experiment with 24
participants who annotated and tagged two distinct maps. We found that the
semantic tagging implementation does not affect these parameters, while
providing tagging relationships to well-defined concept definitions. Compared
to label-based tagging, our technique also gathers positive and negative
tagging relationships. We believe that our findings carry implications for
designers who want to adopt semantic tagging in other contexts and systems on
the Web.Comment: 10 page
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
Semantic Grounding Strategies for Tagbased Recommender Systems
Recommender systems usually operate on similarities between recommended items
or users. Tag based recommender systems utilize similarities on tags. The tags
are however mostly free user entered phrases. Therefore, similarities computed
without their semantic groundings might lead to less relevant recommendations.
In this paper, we study a semantic grounding used for tag similarity calculus.
We show a comprehensive analysis of semantic grounding given by 20 ontologies
from different domains. The study besides other things reveals that currently
available OWL ontologies are very narrow and the percentage of the similarity
expansions is rather small. WordNet scores slightly better as it is broader but
not much as it does not support several semantic relationships. Furthermore,
the study reveals that even with such number of expansions, the recommendations
change considerably.Comment: 13 pages, 5 figure
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
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