Data Mining_ Concepts and Techniques - Jiawei Han [370]
The content-based approach recommends items that are similar to items the user preferred or queried in the past. It relies on product features and textual item descriptions. The collaborative approach (or collaborative filtering approach) may consider a user's social environment. It recommends items based on the opinions of other customers who have similar tastes or preferences as the user. Recommender systems use a broad range of techniques from information retrieval, statistics, machine learning, and data mining to search for similarities among items and customer preferences. Consider Example 13.1.
Scenarios of using a recommender system
Suppose that you visit the web site of an online bookstore (e.g., Amazon) with the intention of purchasing a book that you have been wanting to read. You type in the name of the book. This is not the first time you have visited the web site. You have browsed through it before and even made purchases from it last Christmas. The web store remembers your previous visits, having stored click stream information and information regarding your past purchases. The system displays the description and price of the book you have just specified. It compares your interests with other customers having similar interests and recommends additional book titles, saying “Customers who bought the book you have specified also bought these other titles as well.” From surveying the list, you see another title that sparks your interest and decide to purchase that one as well.
Now suppose you go to another online store with the intention of purchasing a digital camera. The system suggests additional items to consider based on previously mined sequential patterns, such as “Customers who buy this kind of digital camera are likely to buy a particular brand of printer, memory card, or photo editing software within three months.” You decide to buy just the camera, without any additional items. A week later, you receive coupons from the store regarding the additional items.
An advantage of recommender systems is that they provide personalization for customers of e-commerce, promoting one-to-one marketing. Amazon, a pioneer in the use of collaborative recommender systems, offers “a personalized store for every customer” as part of their marketing strategy. Personalization can benefit both consumers and the company involved. By having more accurate models of their customers, companies gain a better understanding of customer needs. Serving these needs can result in greater success regarding cross-selling of related products, upselling, product affinities, one-to-one promotions, larger baskets, and customer retention.
The recommendation problem considers a set, C, of users and a set, S, of items. Let u be a utility function that measures the usefulness of an item, s, to a user, c. The utility is commonly represented by a rating and is initially defined only for items previously rated by users. For example, when joining a movie recommendation system, users are typically asked to rate several movies. The space C × S of all possible users and items is huge. The recommendation system should be able to extrapolate from known to unknown ratings so as to predict item–user combinations. Items with the highest predicted rating/utility for a user are recommended to that user.
“How is the utility of an item estimated for a user?" In content-based methods, it is estimated based on the utilities assigned by the same user to other items that are similar. Many such systems focus on recommending items containing textual information, such as web sites, articles, and news messages. They look for commonalities among