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The role of tags for recommendation: a survey
"... Abstract — Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other pe ..."
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Abstract — Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other peers for browsing available resources. However, due to the absence of rules for managing the tagging process, and to the lack of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications dop not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing. For this reason researchers are modeling innovative recommender systems capable to better support tagging, browsing, and searching for new resources. This paper is a survey which discusses the role of tags in recommender systems: starting from social tagging systems, we analyze various techniques for suggesting content and we introduce the approaches exploited for proposing tags for classifying resources, considering both personalized and notpersonalized recommendation.
Using Inferred Tag Ratings to Improve User-based Collaborative Filtering
"... User-based collaborative filtering is one of the most widelyused recommender methods. It recommends items to a user according to her similar users ’ opinions. The key point of user-based collaborative filtering is to compute users ’ similarities. In traditional user-based collaborative filtering, th ..."
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User-based collaborative filtering is one of the most widelyused recommender methods. It recommends items to a user according to her similar users ’ opinions. The key point of user-based collaborative filtering is to compute users ’ similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations. With the rapid development of social tagging systems, social tagging data poses new opportunities for recommender systems. Many researchers have proposed different methods to exploit tagging data to improve the performance of recommender systems. In this paper, we propose a new approach to compute users ’ similarities using the inferred tag ratings. A user’s preference for a tag t can be inferred based on her ratings of items tagged with t. A user rates too few such items, then her inferred rating to t may be inaccurate. Hence the relationships among tags are used to infer her preference for t based on all her item ratings, such that the preference of user could be accurate. Experiments were done on the MovieLens data set to evaluate the performance of our approach. The results show that our approach outperform traditional user-based collaborative filtering.
SELECTING TRUSTWORTHY CONTENT USING TAGS- Special Session on Trust in Pervasive Systems and Networks-
"... Networked portable devices enable their users to easily create and share digital content (e.g., photos, videos). Hitherto, this serendipitous form of sharing has not happened. That may be because, for sharing content, mobile users have no choice but to go through the Internet. Users are thus in need ..."
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Networked portable devices enable their users to easily create and share digital content (e.g., photos, videos). Hitherto, this serendipitous form of sharing has not happened. That may be because, for sharing content, mobile users have no choice but to go through the Internet. Users are thus in need of decentralised mechanisms for browsing location-based content. To realize such mechanisms, the following two questions must be answered first: how to select “relevant content”, by semantically matching user queries, and how to select “quality content ” from the clutter generated by a potentially huge number of producers. We explore ways to answer these questions. We propose a combined approach that infers “relevance ” by reasoning about the semantics emerging from the tags that users associate to content, and “quality ” by running distributed trust models that recognize trustworthy producers. 1
Modeling a publication sharing system 2.0
"... Abstract — Current publication sharing systems inherit from the Web 2.0 philosophy the idea that users can add reusable information to support other peers, enabling them to insert new resources and to tag the existing ones; but, in their current form, these systems suffer of some limitations, such a ..."
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Abstract — Current publication sharing systems inherit from the Web 2.0 philosophy the idea that users can add reusable information to support other peers, enabling them to insert new resources and to tag the existing ones; but, in their current form, these systems suffer of some limitations, such as the lack of tools for supporting users during the creation and organization of their personal concept spaces, and the poor utilization of tags as information sources for producing personalized recommendations. In this paper we propose a model for organizing dynamic and customizable concept spaces, based on innovative structures, and we introduce a mechanism for recommendation, based on tags and mainly on the way in which users connect resources in their concept spaces. Adaptive recommendations are generated analyzing the users ’ concept spaces, and evaluating the similarities among them in order to reveal the similarity among their goals and perspectives.
