Basic Concept of Recommendation System

What is a Recommendation System

Recommendation System (RSs) are software tools and techniques providing suggestions for items to be of use to a user. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read.

What is a Recommendation System
Recommendation System

Item” is the general term used to denote what the System Recommends to users. A  Recommender Systems normally focuses on a specific type of item (e.g., CDs, or news) and accordingly its design, its graphical user interface, and the core recommendation technique used to generate the recommendations are all customized to provide useful and effective suggestions for that specific type of item.

Recommender Systems are primarily directed towards individuals who lack sufficient personal experience or competence to evaluate the potentially overwhelming number of alternative items that a Web site, for example, In the popular Website, Amazon.com, the site employs a Recommender Systems to personalize the online store for each customer. Since recommendations are usually personalized, different users or user groups receive diverse suggestions. In addition there are also non-personalized recommendations. These are much simpler to generate and are normally featured in magazines or newspapers. Typical examples include the top ten selections of books, CDs etc. While they may be useful and effective in certain situations, these types of non-personalized recommendations are not typically addressed by Recommender Systems research.

In their simplest form, personalized recommendations are offered as ranked lists of items. In performing this ranking, Recommender Systems try to predict what the most suitable products or services are, based on the user’s preferences and constraints. In order to complete such a computational task, Recommender Systems collect from users their preferences, which are either explicitly expressed, e.g., as ratings for products, or are inferred by interpreting user actions. For instance, Recommender Systems may consider the navigation to a particular product page as an implicit sign of preference for the items shown on that page.

Recommender Systems are relatively new compared to research into other classical information system tools and techniques (e.g., databases or search engines). Recommender systems emerged as an independent research area in the mid-1990s. In recent years, the interest in recommender systems has dramatically increased, as the following facts indicate:

  1. Recommender systems play an important role in such highly rated Internet sites as Amazon.com, YouTube, Netflix, Yahoo, Tripadvisor, Last.fm, and IMDb. Moreover, many media companies are now developing and deploying Recommender systems (RSs) as part of the services they provide to their subscribers. For example, Netflix, the online movie rental service, awarded a million dollar prize to the team that first succeeded in improving substantially the performance of its recommender system.
  2. There are dedicated conferences and workshops related to the field. We refer specifically to ACM Recommender Systems (RecSys), established in 2007 and now the premier annual event in recommender technology research and applications. In addition, sessions dedicated to Recommender Systems are frequently included in the more traditional conferences in the area of databases, information systems, and adaptive systems. Among these conferences are worth mentioning ACM SIGIR Special Interest Group on Information Retrieval (SIGIR), User Modeling, Adaptation and Personalization (UMAP), and ACM’s Special Interest Group on Management Of Data (SIGMOD).
  3. At institutions of higher education around the world, undergraduate and graduate courses are now dedicated entirely to Recommender Systems; Tutorials on Recommender Systems are very popular at computer science conferences.
  4. There have been several special issues in academic journals covering research and developments in the Recommender Systems field. Among the journals that have dedicated issues to Recommender Systems are: AI Communications (2008); IEEE Intelligent Systems (2007); International Journal of Electronic Commerce (2006); International Journal of Computer Science and Applications (2006); ACM Transactions on Computer-Human Interaction (2005); and ACM Transactions on Information Systems (2004).

In this Article, We are Briefly Discuss Basic Recommendation System Ideas and Concepts…

 

 

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