Contextual Recommender Systems Using a Multidimensional Approach
Issue:
Volume 2, Issue 4, August 2013
Pages:
55-63
Received:
16 July 2013
Published:
20 August 2013
Abstract: Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendation. The recommender system could simultaneously possess the advantages of content-based recommendation, knowledge-based recommendation, collaborative filtering recommendation and On-Line Analytical Processing (OLAP) in segmenting the information. Following the improvement of the recommendation structure, it doesn’t have to limit its analysis on the user and product to compute for the recommendation result and it could also handle and determine more complex contextual information as recommendation computation foundation. It could develop better results if applied in different domains. This work extends the multidimensional recommendation model concept of Adomavicius and Tuzhilin (2001) and proposes a multidimensional recommendation environment to integrate the contextual information.
Abstract: Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used...
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Some Extensions of Positive and Negative Rules for Discovering Basic Interesting Rules
Issue:
Volume 2, Issue 4, August 2013
Pages:
64-69
Received:
28 May 2013
Published:
30 August 2013
Abstract: Positive reasoning and negative reasoning have been applied to be very useful in practice as clear from the record of many real life applications, especially in medicine. These reasoning mechanisms play important role in cutting the search space, reflecting experts' decision, supporting decision by the cooperation of experts and computers. This paper proposes the concepts of extended negative rule, minimal rule and explores their properties. Furthermore, an algorithm for finding all minimal positive rule and minimal negative rule is given. This algorithm is effective to discover positive and negative rules which have not redundant formula. These rules support to deduce the other important positive and negative rules. Experiments are carried out on data sets of UCI machine learning repository to analyze the performance study.
Abstract: Positive reasoning and negative reasoning have been applied to be very useful in practice as clear from the record of many real life applications, especially in medicine. These reasoning mechanisms play important role in cutting the search space, reflecting experts' decision, supporting decision by the cooperation of experts and computers. This pap...
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