Show Me Your Friends, I’ll Tell You Who You Are: Recommending Products Based on Hidden Evidence

Sekar, Anbarasu; Chakraborti, Sutanu

doi:10.1007/978-3-030-29249-2_22

Cited by 1 publication

(4 citation statements)

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“…In the first part of our work, we use the idea of utilizing higher order relationship among the products to enhance [2]. We solve our problem by posing it as a random surfer model.…”

Section: Random Surfer Modelmentioning

confidence: 99%

“…In the work by authors in [2], the PBF given by the user in every interaction cycle is used to account for the evidence of the products in the domain. In a given interaction cycle, only k products are shown to the user and the user prefers only one product among them, then how is the evidence propagated to other products in the domain?…”

Section: Evidence-based Recommendationmentioning

confidence: 99%

“…Step 1: The preferences of the user are expressed in terms of query Q. Q has one or more values corresponding to the Step 2: Products are ranked based on (2). The top k ranked products are recommended to the user.…”

Section: A Higher Order Evidence Propagationmentioning

confidence: 99%

“…The authors in Evidence-Based Recommendation (EBR) [2] propose a novel view of the feedback provided by the user in CCBR-RSs. A product in the domain needs evidence that it would be preferred by the user before it could be included in RS.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting the Interplay among Products for Efficient Recommendations

Sekar¹,

Chakraborti²

2019

IJACSA

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Recommender systems are built with the aim to reduce the cognitive load on the user. An efficient recommender system should ensure that a user spends minimal time in the process. Conversational Case-Based Recommender Systems (CCBR-RSs) depend on the feedback provided by the user to learn about the preferences of the user. Our goal is to use the feedback provided by the user effectively by exploiting the interplay among the products to build an efficient CCBR-RS. In this work, we propose two ways towards achieving that goal. In the first method, we utilize the higher order similarity and tradeoff relationship among the products to propagate the evidence obtained through user feedback. In our second method, we utilize the diversity among cases/products along with the similarity and trade-off relationship to make the best use of the feedback provided by the user.

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“…In the first part of our work, we use the idea of utilizing higher order relationship among the products to enhance [2]. We solve our problem by posing it as a random surfer model.…”

Section: Random Surfer Modelmentioning

confidence: 99%

Section: Evidence-based Recommendationmentioning

confidence: 99%

Section: A Higher Order Evidence Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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