scholarly journals A Novel Approach of Product Recommendation using Utility Based Association Rules

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rules ◽  
Recommender System ◽  
New Products ◽  
Significant Information ◽  
Product Recommendation ◽  
Frequent Item ◽  
Novel Approach

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.

A Novel Approach on Negative Association Rules

2011 ◽  
pp. 1425-1430
Author(s):  
Ioannis N. Kouris
Keyword(s):  
Association Rules ◽  
Positive Association ◽  
Negative Influence ◽  
Negative Association ◽  
Association Rules Mining ◽  
Novel Approach ◽  
Negative Effect ◽  
Negative Association Rules ◽  
The Subject ◽  
Definition Of

Research in association rules mining has initially concentrated in solving the obvious problem of finding positive association rules; that is rules among items that exist in the stored transactions. It was only several years after that the possibility of finding also negative association rules became especially appealing and was investigated. Nevertheless researchers based their assumptions regarding negative association rules on the absence of items from transactions. This assumption though besides being dubious, since it equated the absence of an item with a conflict or negative effect on the rest items, it also brought out a series of computational problems with the amount of possible patterns that had to be examined and analyzed. In this work we give an overview of the works having engaged with the subject until now and present a novel view for the definition of negative influence among items.

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

Enhancing the Process of Knowledge Discovery in Geographic Databases Using Geo-Ontologies

2009 ◽  
pp. 2405-2426 ◽  
Author(s):  
Vania Bogorny ◽  
Paulo Martins Engel ◽  
Luis Otavio Alavares
Keyword(s):  
Knowledge Discovery ◽  
Prior Knowledge ◽  
Association Rules ◽  
Pattern Mining ◽  
Spatial Association ◽  
Geographic Pattern ◽  
Geographic Patterns ◽  
Geographic Databases ◽  
Novel Approach ◽  
Amount Of Knowledge

This chapter introduces the problem of mining frequent geographic patterns and spatial association rules from geographic databases. In the geographic domain most discovered patterns are trivial, non-novel, and noninteresting, which simply represent natural geographic associations intrinsic to geographic data. A large amount of natural geographic associations are explicitly represented in geographic database schemas and geo-ontologies, which have not been used so far in frequent geographic pattern mining. Therefore, this chapter presents a novel approach to extract patterns from geographic databases using geoontologies as prior knowledge. The main goal of this chapter is to show how the large amount of knowledge represented in geo-ontologies can be used to avoid the extraction of patterns that are previously known as noninteresting.

Corrigendum to “Recommender system based on pairwise association rules” [Expert Systems With Applications 115 (2018) 535–542]

2019 ◽  
Vol 135 ◽  
pp. 410
Author(s):  
Timur Osadchiy ◽  
Ivan Poliakov ◽  
Patrick Olivier ◽  
Maisie Rowland ◽  
Emma Foster
Keyword(s):  
Expert Systems ◽  
Association Rules ◽  
Recommender System
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