42,340 research outputs found
Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform
Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Trusted Knowledge Infusion Model-based Recommender System for IoT based B2B applications
The exponential growth and usage of Internet, social sites, and e-commerce results extensive amount of information everywhere. It becomes very difficult for the people to search something, so they go for the offered suggestions which are pertinent for them rather than searching from a potentially overwhelming number of options. Recommender Systems are the solutions for such difficulties. There are many powerful Recommender Systems available for e-commerce, websites, books, tourism, and documents, but recommendations for IoT-based applications need of new discoveries. Traditional recommendations methods are not sufficient for big data based scalable and heterogeneous IoT environment. In this paper, we propose a knowledge infusion model-based hybrid recommendation model for IoT-based B2B applications. The proposed model is analyzed with a real dataset, and the evaluation represents that model performs well in terms execution time, RMSE, precision and F1-score as compared with the existing models
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
In recent years, it has become easy to obtain location information quite
precisely. However, the acquisition of such information has risks such as
individual identification and leakage of sensitive information, so it is
necessary to protect the privacy of location information. For this purpose,
people should know their location privacy preferences, that is, whether or not
he/she can release location information at each place and time. However, it is
not easy for each user to make such decisions and it is troublesome to set the
privacy preference at each time. Therefore, we propose a method to recommend
location privacy preferences for decision making. Comparing to existing method,
our method can improve the accuracy of recommendation by using matrix
factorization and preserve privacy strictly by local differential privacy,
whereas the existing method does not achieve formal privacy guarantee. In
addition, we found the best granularity of a location privacy preference, that
is, how to express the information in location privacy protection. To evaluate
and verify the utility of our method, we have integrated two existing datasets
to create a rich information in term of user number. From the results of the
evaluation using this dataset, we confirmed that our method can predict
location privacy preferences accurately and that it provides a suitable method
to define the location privacy preference
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