Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing

Yuan, Soe‐Tsyr; Cheng, Chia-Hsin

doi:10.1016/j.eswa.2003.10.006

Cited by 42 publications

(20 citation statements)

References 5 publications

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“…A brand perceived as sincere develops, in time, relations more stable and more intense than the existing brands (Krishnamurthy and Sandeep, 2003). Brands that are seen as 'sincere' will earn relationship advantages similar to friendship development between humans, thus increasing relationship strength (Yuan and Cheng, 2004). Results are presents in table 5.…”

Section: Test Of the Mediating Variablesmentioning

confidence: 94%

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Brand Personality’s Influence on the Purchase Intention: A Mobile Marketing Case

Bouhlel¹,

Mzoughi²,

Hadiji³

et al. 2011

IJBM

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This study underlines the value of the brand personality and its influence on consumer's decision making, through relational variables. An empirical study, in which 380 participants have received an SMS ad, confirms that brand personality does actually influence brand trust, brand attachment and brand commitment. The levels of brand sensitivity and involvement have also an impact on the brand personality and on its related variables.

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Section: Test Of the Mediating Variablesmentioning

confidence: 94%

“…It reflects its cognitive and emotional structures as well as his past behavior (Clickatell, 2004). The enduring involvement predicts regular behavior in reliable, personal, discreet and customized channel (Karjaluoto, Leppäniemi, and Salo, 2004;Yuan and Cheng, 2004).…”

Section: Involvement Towards the Brandmentioning

confidence: 99%

Brand Personality’s Influence on the Purchase Intention: A Mobile Marketing Case

Bouhlel¹,

Mzoughi²,

Hadiji³

et al. 2011

IJBM

View full text Add to dashboard Cite

show abstract

“…Sometimes though, a recommender system may apply some technique to generate/formulate the appropriate representation from some raw data or other representation that describes the items. Examples of such techniques are association rule mining [110], clustering [115], classification [66], as well as, dimensionality reduction [18].…”

Section: Integrating Existing Approaches For Analyzing Recommender Symentioning

confidence: 99%

“…The items in the domain may be represented using some methods that include: (a) a simple index or a list of items that are all at the same hierarchical level [16,53,102]; (b) a taxonomy of items where items belong to a hierarchy of classes of similar items [18,19,68]; or (c) an ontology where more complex relationships are defined between items or classes of items [51,66,115].…”

Section: Integrating Existing Approaches For Analyzing Recommender Symentioning

confidence: 99%

Analysis and Classification of Multi-Criteria Recommender Systems

2007

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Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.

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“…The system predicts items that the target user interested in according to the behavior information of the users in Top-N and recommends those items to the target user [8] [9]. The formula is as follows:…”

Section: Produce Recommendationsmentioning

confidence: 99%