Mining top- k co-occurrence items with sequential pattern

Kieu, Tung; Vo, Bay; Le, Tuong; Deng, Zhihong; Le, Bac

doi:10.1016/j.eswa.2017.05.021

Cited by 33 publications

(20 citation statements)

References 12 publications

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“…This research suggested various SBRs in order to improve recommenders' performance. There are existing studies that focus on session information [34][35][36][37][38]. Our proposed SBR methods are unique compared to previous research because we consider item sessions as well as attribute sessions.…”

Section: Discussionmentioning

confidence: 99%

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Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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Section: Discussionmentioning

confidence: 99%

“…The Association Rules Recommender (ARR) is a representative approach among SBRs. Originally it was developed to discover user consumption patterns within a large transaction database regardless of the order of their appearance [34,35]. Later was extended to consider sequence patterns in the transactions [36][37][38].…”

Section: Session-based Recommender Systemsmentioning

confidence: 99%

Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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“…1 and Sec. 2.2.1, directly applying the existing pattern mining semantics [3,29,15,16,37,4,9,14,36,22,28,13,32,35,11,12,26] to detect typical patterns does not adequately capture outlier patterns, because they do not distinguish between an independent occurrence of a pattern Q and its occurrence as part of some frequent super-pattern P . Frequent Pattern Mining Algorithms.…”

Section: Evaluation Of Inverted Indexmentioning

confidence: 99%

Efficient discovery of sequence outlier patterns

et al. 2019

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Modern Internet of Things (IoT) applications generate massive amounts of time-stamped data, much of it in the form of discrete, symbolic sequences. In this work, we present a new system called TOP that deTects Outlier Patterns from these sequences. To solve the fundamental limitation of existing pattern mining semantics that miss outlier patterns hidden inside of larger frequent patterns, TOP offers new pattern semantics based on contextual patterns that distinguish the independent occurrence of a pattern from its occurrence as part of its super-pattern. We present efficient algorithms for the mining of this new class of contextual patterns. In particular, in contrast to the bottom-up strategy for state-of-the-art pattern mining techniques, our top-down Reduce strategy piggy backs pattern detection with the detection of the context in which a pattern occurs. Our approach achieves linear time complexity in the length of the input sequence. Effective optimization techniques such as context-driven search space pruning and inverted index-based outlier pattern detection are also proposed to further speed up contextual pattern mining. Our experimental evaluation demonstrates the effectiveness of TOP at capturing meaningful outlier patterns in several real-world IoT use cases. We also demonstrate the efficiency of TOP, showing it to be up to 2 orders of magnitude faster than adapting state-of-the-art mining to produce this new class of contextual outlier patterns, allowing us to scale outlier pattern mining to large sequence datasets.

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“…There are many methods [4,5] for mining FPs in recent years. In addition, some issues related to FP mining has been proposed such as maximal frequent patterns [6], top-k cooccurrence items with sequential pattern [7], weightedbased patterns [8], periodic-frequent patterns [9], and their applications [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform

Huynh

2018

Complexity

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Mining erasable itemset (EI) is an attracting field in frequent pattern mining, a wide tool used in decision support systems, which was proposed to analyze and resolve economic problem. Many approaches have been proposed recently, but the complexity of the problem is high which leads to time-consuming and requires large system resources. Therefore, this study proposes an effective method for mining EIs based on multicore processors (pMEI) to improve the performance of system in aspect of execution time to achieve the better user experiences. This method also solves some limitations of parallel computing approaches in communication, data transfers, and synchronization. A dynamic mechanism is also used to resolve the load balancing issue among processors. We compared the execution time and memory usage of pMEI to other methods for mining EIs to prove the effectiveness of the proposed algorithm. The experiments show that pMEI is better than MEI in the execution time while the memory usage of both methods is the same.

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Mining top- k co-occurrence items with sequential pattern

Cited by 33 publications

References 12 publications

Session-Based Recommender System for Sustainable Digital Marketing

Session-Based Recommender System for Sustainable Digital Marketing

Efficient discovery of sequence outlier patterns

An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform

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