Abstract
Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.
Highlights
Nowadays, we are facing too much information from online systems
We propose the concept of information backbone which is supposed to preserve the essential information needed for recommendation
The rapid expansion of the internet leads to an increasing amount of information from the World Wide Web
Summary
We are facing too much information from online systems. We have to make choices from thousands of movies, millions of books, billions of web pages, and so on. There are always some very popular items, which are almost collected by every user (e.g. some super popular movies watched by everyone) In this case, if a user bought such item, the recommender system cannot extract much information about the user’s preference from this purchase action. By using the information in the backbone structures, the recommender systems are able to make as accurate prediction of users’ interested items as the original networks. The timeaware algorithms work better in preserving recommendation accuracy while the topology-aware algorithms have advantage in enhancing the recommendation diversity We hybrid these two type of algorithms and achieve a further improvement in preserving the information for recommendation. By using the hybrid algorithm, we obtain the above-mentioned information backbone from the real user-object bipartite networks (The number of links is reduced by about 80%). We remark that our method is very meaningful from the practical point of view since it can largely reduce the computational cost of the recommender systems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
