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chapter03-part01-recommendation_推荐系统
Mining the Web Chakrabarti and Ramakrishnan Recommendation 朱廷劭(Zhu, Tingshao)Ph.D Recommender Systems “What did you think about...?” “Did you like...?” Make recommendation based on past experience Real world examples: food critic, movie critic, book/novel critic, lecture course critic :-) Recommender Systems How do you know you can trust somebody’s recommendation? Because experience has taught you? Because critic is trusted source of info? Because a friend/expert likes movies/novels/ food you like? ??? Applications: A Book Recommender Personalization Recommenders are instances of personalization software. Personalization concerns adapting to the individual needs, interests, and preferences of each user. Includes: Recommending Filtering Predicting (e.g. form or calendar appt. completion) From a business perspective, it is viewed as part of Customer Relationship Management (CRM). What is Recommendation? Recommendations are suggestions It could be a suggestion to watch a particular movie, or to buy a particular product, visit a restaurant (not fish!) In hyperspace, this could be a suggestion to follow a path leading to a relevant document, or to visit a document directly What is Recommendation? If the recommendation is to do with guidance, then this is related to adaptive navigation If the recommendation is based mainly on recommending products, then it is a recommender system The two are, or can be, closely related, but the literature tends to deal with them separately Architecture Recommender Systems Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences. Many on-line stores provide recommendations (e.g. Amazon, CDNow). Recommenders have been shown to substantially increase sales at on-line stores. There are two basic approaches to recommending: social filtering (e.g. Collaborative Filtering) Content-based Non-Personalized Use popularity People’s ratings, sales data Accumulate rating
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