Recommender software

Recommender system appears in:. Encyclopedia of Information Science and Search inside this book for more research materials. Recommend to a Librarian Recommend to a Colleague. Looking for research materials? Search our database for more Recommender system downloadable research papers. Full text search our database of , titles for Recommender system to find related research papers.

Exploring Online Learning Through Synchronou Exploring online learning through the lens of sync In Stock. Study Abroad Opportunities for Community Col One software that Express Analytics uses in developing recommenders for clients is the Neo4j software. This relationship creates a pattern that associates two chunks of information together and defines a flow by assigning a direction to it. We can add pieces of information to our nodes and relationships by assigning properties to them.

But more on the Neo4j later. Data alone does not drive your business. Decisions do. Speak to Our Experts to get a lowdown on how recommender system can help your business. Just to make it easy to understand it better, for this blog post, we will help explain how we design a recommendation engine for an e-commerce client. One evening, you plan to dine out. Being new to the city you do not know which restaurant to choose.

Does that mean for an analyst it is impossible to predict what kind of cuisine you want to try out tonight? It is rational for an analytics model to have uncertainty in the mix as several factors are involved to derive the results. But those uncertainties can be removed by building more algorithms or putting more context to the filtering, or generating more recommendations per algorithm to increase diversity.

Before going ahead with the explainer on how to build a recommendation engine , let us learn some of the various types:. Below flow chart can make the classification and sub-classifications of recommender systems a bit clearer:. Recommendations through content-based filtering techniques are influenced by what the user has browsed earlier, or what he is currently browsing. Unlike content-based filtering which only takes into account user-specific item interactions, the collaborative filtering technique follows a more mature approach and finds out similar users based on user-item interactions.

An example: consider two user browsing patterns. We can increase the confidence level of the two users being similar by comparing the number of common products they have browsed. Once you mix concepts of content-based filtering and collaborative filtering to generate recommendations you have developed a hybrid system.

Consider the above graph data structure. Here, we have four nodes, i. State, Customers, Category, and Products, and three relationships with directions connecting the four nodes. Their respective properties are in the assigned dialogue type box. We want to recommend products to the customers although we can recommend them anything, making rich recommendations will increase the probability of them buying one.

Built on top of Node. Talking about how the engine works, it makes use of the Jaccard coefficient to know the similarity between users and k-nearest-neighbours to create recommendations. Raccoon takes care of all the recommendation and rating logic.

Written in Java, easyrec is a free and open source web application that provides personalized recommendations using RESTful Web Services and can be integrated into any web-enabled applications. How do easyrec works? Using the REST API, user actions such as viewing, buying or rating an item are sent to the easyrec and are stored in the database of the engine.

Then, the analyzer periodically analyzes all recorded data and identifies patterns to generate recommendations. LensKit is a Java-based research recommender system. It also provides support for training, running, and evaluating recommender algorithms.

LensKit can be used for research recommender algorithms, evaluation techniques, or user experience, and also to build the next recommender application.

Not exactly a recommender system itself, Crab is a python framework that is used to build a recommender system. The main focus of the framework is to provide a way to build customised recommender system from a set of algorithms. The prime use of this state-of-the-art open source stack is for developers and data scientists to create predictive engines, which we also call as a recommender system for any machine learning task.

PredictionIO is fast and engines can be deployed as a web service during production. Also, it is open source that gives you the privilege to take a look at the code and know how it works. Then the system gives recommendations relying on the best matches.

The idea is that users with common interests are more likely to choose the same items in the future. How does the system find these preferences? There are two main categories of collaborative filtering:. Each item has its own category — genre, singer, album, year, and so on — and description, a textual piece of information about the product.

Using these attributes, the system will try to find the best match for the user according to their interests and offer the best item available. As collaborative filtering and content-based approaches differ at their core, many businesses prefer to use a mix of the two, making their recommendation systems more effective. The content-based approach is often implemented over collaborative filtering to sort out the results of the latter. Another approach to recommendations is based on knowledge.