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A guide to constructing a recommendation system that helps in knowledge discovery

Introduction

A recommendation engine helps in delivering information to every nook and corner of the world. To describe this in figures, 60% of the world population is online according to the Digital Dividends report. More than 90% of the population of the world that is online depends on Google to mine useful knowledge. There have been very few competitors to Google given its established popularity over the decades. As such, there have been very few efforts to develop a recommendation engine that would surpass Google. This article is an attempt to help in the construction of a recommendation engine that helps in knowledge discovery.

An overview of the metrics

The first job at hand is to conceive a system that not only collects data but also stores it for long-term use. This is important for not only contrasting different recommendations but also for the development of threads for recommendation engines. In order to develop these threads, the analysis of the internal sources of different pages is the priority. After the sources of different pages have been analyzed, we push this information to a tracker. After this, the information is passed to a message queue. The purpose of passing this information to a message queue is the development of different programs in the future with the help of this data. There are three main metrics that are associated with the message queue. The first is called the metric and analytic system. The second component is referred to as the anonymous usage data store. The third component is called the active usage data store.

Initial testing framework

After the different metrics have been developed, the next stage is called the initial testing stage. This stage allows us to store, search and visualize different types of incoming messages or data. This is helpful in the creation of a visual dashboard that gives exact figures about the number of people that have read a piece of information from the source. In order to label these messages, we can add an extra piece of information to them. This labeling of messages serves two purposes. Firstly, it helps in the contrasting of different versions of a widget. Secondly, it helps to keep track of each message which helps in comparing the performance of widgets at a later stage.

The process of filtering

The process of filtering relies on the use of different algorithms as per the needs of the recommendation engine. Algorithms not only help in collaborative filtering but also help in the development of a similarity index. Let us understand this by a simple example. Whenever a genre of articles is read by a group of users, these users are clustered into a specific segment based on a particular reading pattern. This not only helps in user segmentation but also helps in recording the frequency of visits for a particular article. This segmentation serves two important purposes. Firstly, it helps in targeting the users with specific keywords and particular articles related to these keywords. Secondly, it also helps in improving the efficiency of the recommendation system by properly targeting the users with interesting information.

We may also use different types of cluster detection algorithms to send various types of recommended articles to different segments of people that we had classified earlier. In this way, we conceive a smaller recommendation engine aggregator that enables us to target the source with a particular set of recommendations.

Similarity Index

There is a high possibility that people bookmark specific articles based on their interest. These bookmarks can be tagged, and these tags can be used as a probe to mine the database for similar articles. These similar articles are displayed as recommendation threads to different types of users based on a similarity index. This similarity index gets modified as the user navigates from one article to another. However, we can still predict the genre of content that a user is interested in. One more benefit of considering a similarity index is that it helps in personalized recommendations. At a later stage, the similarity index can be used to create better recommendations and customized suggestions for the users.

The process of conceiving a recommendation engine is simple yet challenging at the same time. It involves the knowledge of technical aspects and requires strong programming capabilities. This article is an attempt to simplify this process and give the users an insight into the technical side of a recommendation engine.

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