DiRec: A Distributed User Interface Video Recommender
نویسندگان
چکیده
Distributed User Interfaces (DUIs) are graphical interfaces whose components are distributed in one or many of the UI distribution dimensions: Time, space, platforms, displays, or users. In this work, we have investigated the impact of the application of DUIs, with respect to the different DUI dimensions, on the experience of users of recommender systems. We developed two prototype video recommendation mobile applications: Monolithic Interface Recommender (MiRec), and Distributed Interface Recommender (DiRec). Sharing mostly the same interface, DiRec additionally offers the possibility of migrating parts of the UI between the mobile application and a larger display (LD). A user study was conducted in which participants used and evaluated both MiRec and DiRec. Our results show a significant difference between DiRec and MiRec in attractiveness (general impression and likability), stimulation, and novelty measures, which posits the existence of a strong interest in DUI recommender systems. Nonetheless, MiRec was found more easy-to-learn and easier to understand than DiRec which gives room for further investigation to pinpoint the reasons of DiRec’s relatively lower perspicuity measures.
منابع مشابه
سیستم پیشنهاد دهنده زمینهآگاه برای انتخاب گوشی تلفن همراه با ترکیب روشهای تصمیمگیری جبرانی و غیرجبرانی
Recommender systems suggest proper items to customers based on their preferences and needs. Needed time to search is reduced and the quality of customer’s choice is increased using recommender systems. The context information like time, location and user behaviors can enhance the quality of recommendations and customer satisfication in such systems. In this paper a context aware recommender sys...
متن کاملContext-Aware Recommender Systems: A Review of the Structure Research
Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...
متن کاملVoting Operations for a Group Recommender System in a Distributed User Interface Environment
This work investigates distributed user interfaces for group recommender systems. In our scenario of a movie recommender, the user interface is distributed on two platforms: personal mobile devices and a public multi-touch tabletop. Our solution proposes voting operations to better support the consensus building among group members. We have implemented a prototype and conducted a preliminary us...
متن کاملIncreasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms
Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...
متن کاملA social recommender system based on matrix factorization considering dynamics of user preferences
With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems...
متن کامل