583 research outputs found
Mining top-k granular association rules for recommendation
Recommender systems are important for e-commerce companies as well as
researchers. Recently, granular association rules have been proposed for
cold-start recommendation. However, existing approaches reserve only globally
strong rules; therefore some users may receive no recommendation at all. In
this paper, we propose to mine the top-k granular association rules for each
user. First we define three measures of granular association rules. These are
the source coverage which measures the user granule size, the target coverage
which measures the item granule size, and the confidence which measures the
strength of the association. With the confidence measure, rules can be ranked
according to their strength. Then we propose algorithms for training the
recommender and suggesting items to each user. Experimental are undertaken on a
publicly available data set MovieLens. Results indicate that the appropriate
setting of granule can avoid over-fitting and at the same time, help obtaining
high recommending accuracy.Comment: 12 pages, 5 figures, submitted to Advances in Granular Computing and
Advances in Rough Sets, 2013. arXiv admin note: substantial text overlap with
arXiv:1305.137
Preference Mining Using Neighborhood Rough Set Model on Two Universes
Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method
Parametric Rough Sets with Application to Granular Association Rule Mining
Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable
Logical Interpretation of Relational Databases
The reformulation of data management type databases in a formal, logical calculus is described. Advantages of this logical form are to provide a framework for automatic inferencing on the database as well as a formal clarification of the databases semantics. Principle applications are to artificially intelligent managerial decision support systems
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