We leave in a world where the quantity of available information is hugely massive. How many movies, songs, news articles, apps are out of there and what is the best way to find content relevant for every single user?
Search is not the only solution. Search assumes that you are already aware of what you are looking for. Perhaps, you already heard the latest song made by Nicky Jam and you search few words, or you want to see the latest movie of Paolo Sorrentino and so you search the title. However, the problem is that you need to know in advance what you are looking for and, then, explicitly submit a query to pull (retrieve) the content. What if there is some piece of information which is very relevant but you are not aware of it? Search will not necessarily help.
For overcoming this limitation, Netflix, Spotify, Google Play, Apple Genius, Amazon they use recommender based technologies that are used to suggest fresh and relevant information to the users with no need of explicitly submitting queries. You can watch your favourite movie, listen your song, read your news articles, and discovery new items to buy even if you are not aware of what is relevant for you in advance.
Surprisingly enough, recommenders are still not yet largely adopted by the Research Communities. How many new and fresh papers are relevant for your research discipline and how long it takes to discover them? Traditionally, discovery is based on word-of-mouth communications where someone in your community will suggest what paper to read and what the new research trends are. But this requires time, and time is fundamental in research. That's why we worked hard to create a break-through technology with our team in London. We needed to solve this problem and help the communities.
So, Team shipped an Academic Recommendation engine which adopts sophisticate machine learning algorithm to learn how to discover scientific articles that are relevant for you. Moreover, Recommendation is personalized and it is based on your own scientific interests. What is cool is that the algorithms makes recommendations tailored on you, the Researcher.
Let's see how this works. Browse mendeley.com/suggest/
First, recommendations are based on what I have read previously and stored in my library. It's clear that I have an interest in data mining and usage statistics. Plus, there a surprising article related to some new types of research topics that was considering recently. That's the serendipity effect.
Then, there are also recommendations based on my research discipline (Computer Science)
More important, experimental data showed that freshness is very important for research. So, we developed a special set of recommenders focused on my own very recent research activity. In my case, this is related to different methodologies for sampling the Web size, and search - of course. Then, we also show what is trending in my discipline right now.
Obviously, we encourage the users to interact with our system and fine tune the suggestion so that the quality of the personalized recommendations can improve over time. The more you interact, the merrier the suggestions will be.
So, Try this cool technology which I believe will disrupt the way in which research is done and will help researchers to save time