Show Me Your Friends, I’ll Tell You Who You Are: Recommending Products Based on Hidden Evidence
08 Sep 2019-pp 329-342
TL;DR: This work aims to make the most use of user-provided feedback with the help of the hidden evidence in the casebase to minimize the cognitive load on the user.
Abstract: One of the goals of a recommender system is to minimize the cognitive load on the user and hence we cannot expect the users to give extensive feedback. The lesser the feedback, the lesser we know about the preferences of the user to make useful recommendations. This work aims to make the most use of user-provided feedback with the help of the hidden evidence in the casebase. The evidence for each product is acquired based on the relation among the products in the domain. The effectiveness of our approach is demonstrated through evaluation on three product domains.
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TL;DR: This work utilizes the higher order similarity and trade-off relationship among the products to propagate the evidence obtained through user feedback and utilizes the diversity among cases/products along with the similarity and Trade-offs relationship to make the best use of the feedback provided by the user.
Abstract: 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 trade-off 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.
2 citations
Cites background or methods from "Show Me Your Friends, I’ll Tell You..."
...In the first part of our work, we use the idea of utilizing higher order relationship among the products to enhance [2]....
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...The authors in Evidence-Based Recommendation (EBR) [2] propose a novel view of the feedback provided by the user in CCBR-RSs....
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...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....
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01 Jan 2019
TL;DR: This work exploits the relationship among the cases/products in addition to the feedback provided by the user in several ways to develop an efficient Conversational Case-Based Recommender system.
Abstract: Recommender systems (RSs) are built with the aim to reduce the cognitive load on the user. An efficient RS 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. In our work, we exploit the relationship among the cases/products in addition to the feedback (preference-based feedback (PBF)) provided by the user in several ways to develop an efficient CCBR-RS.
1 citations
Cites background from "Show Me Your Friends, I’ll Tell You..."
...“Show me your friends, I’ll tell you who you are: Recommending products based on hidden evidence” [9]...
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References
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TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
9,873 citations
Posted Content•
TL;DR: In this article, a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
Abstract: Many of the complex problems faced by decision makers involve multiple conflicting objectives. This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives. The theory is illustrated by many real concrete examples taken from a host of disciplinary settings. The standard approach in decision theory or decision analysis specifies a simplified single objective like monetary return to maximise. By generalising from the single objective case to the multiple objective case, this book considerably widens the range of applicability of decision analysis.
2,401 citations
02 Aug 2001
TL;DR: This paper proposes and evaluates strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency and argues that often diversity can be as important as similarity.
Abstract: Case-based reasoning systems usually accept the conventional similarity assumption during retrieval, preferring to retrieve a set of cases that are maximally similar to the target problem. While we accept that this works well in many domains, we suggest that in others it is misplaced. In particular, we argue that often diversity can be as important as similarity. This is especially true in case-based recommender systems. In this paper we propose and evaluate strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency.
416 citations
01 Jan 2007
TL;DR: This chapter describes the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems.
Abstract: Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems.
186 citations