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Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a...
In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles...
Due to the huge product assortments and complex descriptions of mobile products/services, it is a great challenge for new customers to select appropriate products. To solve this issue, a fuzzy matching based recommendation approach for mobile products/services is proposed in this paper. In this approach, a new customer's requirements are obtained through asking a set of questions and represented by...
Multicriteria Collaborative Filtering is a promising approach to recommender systems that explores user ratings on item components in order to generate high quality recommendations. This paper focuses on multicriteria collaborative recommender systems and proposes a new algorithm that estimates aggregation functions, which represent the relative importance of individual components, based on the concept...
This work presents a novel proposal for incremental intruder detection in collaborative recommender systems. We explore the use of rare association rule mining to reveal the existence of a suspected raid of attackers that would alter the normal behaviour of a rating-based system. In this position paper we have extended our previous G3PARM algorithm, which has already proven to serve as a solid method...
Composition style is often an important factor in readers' selection of reading materials. For example, a reader may seek out articles written in similar style as his or her favorite writer. We present a new method for providing recommendations based on the composition style. Our algorithm analyzes and encodes the readability index and syntactical structure of a model document, and then searches for...
Thanks to the drastic proliferation of Internet, e-learning has been recognized as an effective media for various kinds of learners. However, the tremendous course materials in the Internet may make learners be confused in choosing their suitable course materials. In this paper, we propose an approach to construct an adaptive curriculum portfolio recommendation system. It offers tailored course materials...
The constant growth of the Internet has made recommender systems very useful to guide users coping with a large amount of data. In this paper, we present a domain independent collaborative and semantic-based recommender system which uses distinct and complementary modules. The approach targets users with various interests and is based on: (i) a collaborative module using association rules in order...
Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent...
Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of...
A challenge in the recommender systems currently available for the tourism domain is how to suggest tourist itineraries in a specific geographical area (city or region). The proposed theoretical model allows items of intangible cultural heritage (events) such as processions, festivals, special markets, etc. to be characterized and correlated. The model features both a set of functions characterizing...
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