A hybrid of sequential rules and collaborative filtering for product recommendation

Liu, Duen‐Ren; Lai, Chin-Hui; Lee, Wang-Jung

doi:10.1016/j.ins.2009.06.004

Cited by 138 publications

(55 citation statements)

References 43 publications

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“…4, No. 6, December 2014 example, a linear combination of rule-based and collaborative filtering recommendations considers customers' purchase sequences over time as well as their purchase data for latest period [17].…”

Section: Recommendations Based On Purchase Patterns Haiyun Lumentioning

confidence: 99%

Recommendations Based on Purchase Patterns

Lu¹

2014

IJMLC

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Abstract-We propose an approach of recommendation based on purchase patterns. The purchase history of users is analyzed to find their purchase patterns related to user behavior. These patterns are then used to predict the category of next possible purchase in a particular location. The proposed approach is experimented on real transaction data. Synthetic and simulation tests are conducted to evaluate the performance. Results show that it performs better than the baseline sequential pattern analysis.  Index Terms-Recommendation systems, purchase patterns, sequential pattern analysis.

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Section: Recommendations Based On Purchase Patterns Haiyun Lumentioning

confidence: 99%

Recommendations Based on Purchase Patterns

Lu¹

2014

IJMLC

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“…Another work focused on the segmentation of users with the k-nearest neighbor method for collaborative filtering. It implemented association rules to find the top N items based on customers' content usage behavior 21 . However, when association rules alone are used in recommendation systems for mobile content, a significant amount of computation may be required to find all possible rules.…”

Section: Techniques Used In Recommendation Systemsmentioning

confidence: 99%

A Framework for Integrated Mobile Content Recommendation

Paireekreng¹

2013

ijecs

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Content filtering in a mobile recommendation system plays a vital role in providing solution to help mobile device users obtain their desire content. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. These problems can be viewed as a user's first time connection to a mobile recommendation system and initial rating of the content in an early stage of the system. Hence, to obtain personalized content for mobile user, mobile content filtering is needed. This paper proposes a framework for integrated mobile content recommendation. The framework makes use of classification and adaptive association rule techniques to build an integrated model. The results demonstrate that the proposed framework outperforms related techniques. This can address the problem of sparsity for mobile content recommendation systems.

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“…developed a novel algorithm for generating all RFM sequential patterns from customers' purchasing data. Liu et al (2009) proposed a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based K-Nearest Neighbors-Collaborative Filtering (KNN-CF) method. In their proposed method, sequential rules are extracted using customers' RFM values from the purchase sequences in the database.…”

Section: Association Rule Mining Using Rfmmentioning

confidence: 99%

Data Mining Using RFM Analysis

Birant¹

2011

Knowledge-Oriented Applications in Data Mining

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A hybrid of sequential rules and collaborative filtering for product recommendation

Cited by 138 publications

References 43 publications

Recommendations Based on Purchase Patterns

Recommendations Based on Purchase Patterns

A Framework for Integrated Mobile Content Recommendation

Data Mining Using RFM Analysis

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