Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that user would give to an item . Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are books, research articles, movies, music, news, search queries, social tags, and products on e-commerce websites in general. Recommender systems assist and
augment this natural social process to help people sift through available books, articles, web pages, movies, music,
restaurants, jokes, grocery products, and so forth to find the most interesting and valuable information for them.
Recommender systems are widely used by online stores for they improve user convenience and store benefits. It turns browsers into buyers, cross-sell items that are suggested at the checkout page, increase users’ loyalty by making the purchase only a few clicks away or awarding frequent customers with good deals and such. E-commerce recommendation algorithms often operate in a challenging environment, especially for large online shopping companies like eBay and Amazon. Usually, a recommender system providing fast and accurate recommendations will attract the interest of customers and bring benefits to companies. Usually Recommender systems produce a list of recommendations in one of three ways: Collaborative filtering (CF), Content-based filtering, and Hybrid recommender systems. RSs 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, may offer
By:P. N. Vijaya Kumar 1, Dr. V. Raghunatha Reddy 2
File Information :English Language/7 Page/Size :260 K
کاری از :P. N. Vijaya Kumar 1, Dr. V. Raghunatha Reddy 2
اطلاعات فایل :زبان انگلیسی /7صفحه /حجم :260 K
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