Web recommendation system: Introduction
The term recommendation is used for describing the suggestions of a particular product or service. therefore the web recommendation systems are a essential part of e-commerce applications. The users who search about some kinds of product or services the recommendation systems helps them by suggesting the most appropriate product or services. In most of the cases the web based recommendation systems are developed using the web usage mining and content mining techniques. In this context using this concept a number of applications are created. The recommendations systems can be described in three major categories. There is an extensive class of Web applications that involve predicting user responses to options. Such a facility is called a recommendation system. However, to bring the problem into focus, two good examples of recommendation systems are :
- Offering news articles to on-line newspaper readers, based on a prediction of reader interests.
- Offering customers of on-line retailer suggestions about what they might like to buy, based on their past history of purchases and/or product searches.
Recommendation systems use a number of different technologies. That can be classify these systems into two broad groups
- Content-based systems examine 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 recommender systems: A hybrid approach is combination of collaborative filtering and content-based filtering. Hybrid approaches are developed in different manners such as by combining the predictive outcomes of collaborative and content-based approaches or by including the properties of content/ collaborative filters to a collaborative/content based approach. The hybrid filters are much accurate then the pure collaborative and content-based approaches.
Applications of Recommendation Systems
There are various application of recommendation systems are available, several important applications of recommendation systems are given as .
figure 1 example recommendation 
- Product Recommendations: Perhaps the most important use of recommendation systems is at on-line retailers. Noted how Amazon or similar on-line 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 customers 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.
- 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.
 Chapter 9- Recommendation Systems, http://i.stanford.edu/~ullman/mmds/ch9.pdf
P. N. Vijaya Kumar, Dr. V. Raghunatha Reddy, “A Survey on Recommender Systems (RSS) and Its Applications”, International Journal of Innovative Research in Computer and Communication Engineering(An ISO 3297: 2007 Certified Organization)Vol. 2, Issue 8, August 2014