Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

Minig Top-K High Utility Itemsets - Report

Utility mining, which refers to the discovery of itemsets with utilities higher than a user-specified minimum utility threshold, is an important task and has a wide range of applications, especially in e-commerce. But setting an appropriate minimum utility threshold is a difficult problem. If the minimum threshold is set to low, too many high utility itemsets will be generated and it takes a lo...

Recommendation System for High Utility Itemsets over Incremental Dataset

Mining high utility itemsets has gained much significance in the recent years. When the data arrives sporadically, incremental and interactive utility mining approaches can be adopted to handle users‟ dynamic environmental needs and avoid redundancies, using previous data structures and mining results. The dependence on recommendation systems has exponentially risen since the advent of search e...

Leveraging High-Dimensional Side Information for Top-N Recommendation

Top-N recommender systems typically utilize side information to address the problem of data sparsity. As nowadays side information is growing towards high dimensionality, the performances of existing methods deteriorate in terms of both effectiveness and efficiency, which imposes a severe technical challenge. In order to take advantage of high-dimensional side information, we propose in this pa...

Mining Minimal High-Utility Itemsets

Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high...

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