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Web Technologies phần 10

một người dùng và là một phần của khái niệm QoR. Thông tin phản hồi của người sử dụng (đánh giá) được hiểu như là sự hài lòng của họ từ kết quả trả về (không phải từ các giao diện mà qua đó họ giao tiếp với một dịch vụ) được thể hiện trong một quy mô được xác định. Tuy nhiên, sẽ rất khó khăn, | Web Content Recommendation Methods Based on Reinforcement Learning tions Burke 2000 . Most ofthese recommenders employ some kind of knowledge-based decision rules for recommendation. This type of recommendation is heavily dependant on knowledge engineering by system designers to construct a rule base in accordance to the specific characteristics of the domain. While the user profiles are generally obtained through explicit interactions with users there have also been some attempts at exploiting machine learning techniques for automatically deriving decision rules that can be used for personalization e.g. Pazzani 1999 . In Content-based filtering systems the user profile represents a content model of items in which that user has previously shown interest Pazzani Bilsus 2007 .These systems are rooted in information retrieval and information filtering research. The content model for an item is represented by a set of features or attributes characterizing that item. The recommendation generation is usually comprised of comparing extracted features from new items with content model in the user profile and recommending items that have adequate similarity to the user profile. Collaborative techniques Resnick Varian 1997 Herlocker et al. 2000 are the most successful and the most widely used techniques in recommender systems e.g. Deshpande Kary-pis 2004 Konstan et al. 1998 Wasfi 1999 . In the simplest from in this class of systems users are requested to rate the items they know and then the target user will be recommended the items that people with similar tastes had liked in the past. Recently Web mining and especially web usage mining techniques have been used widely in web recommender systems Cooley et al. 1999 Fu et al. 2000 Mobasher et al. 2000a Mobasher et al. 2000b . Common approach in these systems is to extract navigational patterns from usage data by data mining techniques such as association rules and clustering and making recommendations based on the extracted .