Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are
Beyond Accuracy
Diversity – Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, i.e. items from e.g. different artists. Recommender Persistence – In some situations it is more effective to re-show recommendations, or let users re-rate
Hybrid Recommendation Systems
Netflix is a good example of hybrid systems. They make recommendations by comparing the watching and searching habits of similar users (i.e. collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated
Collaborative filtering
When building a model from a user’s profile, a distinction is often made between explicit and implicit forms of data collection. Examples of explicit data collection include the following: Asking a user to rate an item on a sliding scale.
Content-based filtering
To abstract the features of the items in the system, item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation).
What is it all about?
SOSERE is a easy way to engage your readers and show them related content from your website. It displays a link list or thumbnails to related pages, posts and custom post types at the bottom of an entry.