5,358 research outputs found
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
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
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
Recommendation systems have wide-spread applications in both academia and
industry. Traditionally, performance of recommendation systems has been
measured by their precision. By introducing novelty and diversity as key
qualities in recommender systems, recently increasing attention has been
focused on this topic. Precision and novelty of recommendation are not in the
same direction, and practical systems should make a trade-off between these two
quantities. Thus, it is an important feature of a recommender system to make it
possible to adjust diversity and accuracy of the recommendations by tuning the
model. In this paper, we introduce a probabilistic structure to resolve the
diversity-accuracy dilemma in recommender systems. We propose a hybrid model
with adjustable level of diversity and precision such that one can perform this
by tuning a single parameter. The proposed recommendation model consists of two
models: one for maximization of the accuracy and the other one for
specification of the recommendation list to tastes of users. Our experiments on
two real datasets show the functionality of the model in resolving
accuracy-diversity dilemma and outperformance of the model over other classic
models. The proposed method could be extensively applied to real commercial
systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure
Encouraging password manager adoption by meeting adopter self-determination needs
Password managers are a potential solution to the password conundrum, but adoption is paltry. We investigated the impact of a recommender application that harnessed the tenets of self-determination theory to encourage adoption of password managers. This theory argues that meeting a person's autonomy, relatedness and competence needs will make them more likely to act. To test the power of meeting these needs, we conducted a factorial experiment, in the wild. We satisfied each of the three self determination factors, and all individual combinations thereof, and observed short-term adoption of password managers. The Android recommender application was used by 470 participants, who were randomly assigned to one of the experimental or control conditions. Our analysis revealed that when all self-determination factors were satisfied, adoption was highest, while meeting only the autonomy or relatedness needs individually significantly improved the likelihood of adoption
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