A web recommendation system is an extensive class of Web applications. That involves predicting user responses to options. Such a facility is called a recommendation system. We can take a basic idea of a recommendation system by using two good examples :
- Online news publishing websites are offering news articles to their readers, based on readers’ interests.
- Offering customers of e-commerce to suggest what they might like to buy. This prediction is performed based on their past history.
Types of Recommendation system
Recommendation systems can be designed by using a number of different techniques. these techniques are related to web mining and data mining algorithms. based on their design concept we can classify these systems into three broad groups:
Figure 1 web recommendation system overview
- Content-based systems examine the properties of the items recommended. For instance, if a Netﬂix user has watched many cowboy movies, then recommend a movie classiﬁed in the database as having the “cowboy” genre.
- Collaborative ﬁltering systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users. However, these technologies by themselves are not suﬃcient, and there are some new algorithms that have proven eﬀective for recommendation systems.
- hybrid filtering systems are a combination of both content-based and collaborative filtering systems. That can be developed in different techniques such as:
- first, implement the content-based technique and then implement properties of collaborative filters, and vice versa
- second implement both the filters separately and combine the consequences of both filters
Applications of Recommendation Systems
There is the various application of recommendation systems available. Several important applications of recommendation systems are given below:
- Product Recommendations: Perhaps the most important use of recommendation systems is at online retailers. Noted how Amazon or similar online vendors strive to present each returning user with some suggestions of products that they might like to buy. These suggestions are not random but are based on the purchasing decisions made by similar customers or on other techniques.
- Movie Recommendations: Netﬂix oﬀers its customer’s recommendations of movies they might like. These recommendations are based on ratings provided by users, much like the ratings suggested. The importance of predicting ratings accurately is so high, that Netﬂix oﬀered a prize of one million dollars for the ﬁrst algorithm that could beat its own recommendation system by 10%. The prize was ﬁnally won in 2009, by a team of researchers called “Bellkor’s Pragmatic Chaos,” after over three years of competition.
- News Articles: News services have attempted to identify articles of interest to readers, based on the articles that they have read in the past. The similarity might be based on the similarity of important words in the documents, or on the articles that are read by people with similar reading tastes. The same principles apply to recommending blogs from among the millions of blogs available, videos on YouTube, or other sites where content is provided regularly.