Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods

Huang, Shiu-Li

doi:10.1016/j.elerap.2010.11.003

Cited by 94 publications

(43 citation statements)

References 43 publications

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“…The lack of ratings in content-based and collaborative filtering approaches results in the incomplete user profile and the new user problem [15]. Such approaches as multi-attribute utility theory allow the user to evaluate only a few items based on some criteria to get the overall representation of preferences.…”

Section: Content-based Recommender Systemsmentioning

confidence: 99%

“…Such approaches as multi-attribute utility theory allow the user to evaluate only a few items based on some criteria to get the overall representation of preferences. Huang [15] followed this approach in order to construct a utility-based recommender system. The main problems were to minimise the effort of the user at the stage of weighting the importance of attributes for the utility function (usually created via multiple regression analysis, artificial neural networks, etc.)…”

Section: Content-based Recommender Systemsmentioning

confidence: 99%

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TWIN: Personality-based Intelligent Recommender System

Roshchina

Cardiff

Rosso

2015

IFS

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Abstract. This paper presents the "Tell me What I Need" (TWIN) Personality-based Intelligent Recommender System, the goal of which is to recommend items chosen by like-minded (or "twin") people with similar personality types which we estimate from their writings. In order to produce recommendations it applies the results achieved in the personality from the text recognition research field to Personality-based Recommender System user profile modelling. In this way it creates a bridge between the efforts of automatic personality score estimation from plain text and the field of Intelligent Recommender Systems. The paper describes the TWIN system architecture, and results of the experimentation with the system in the online travelling domain in order to investigate the possibility of providing valuable recommendations of hotels of the TripAdvisor website for "like-minded people". The results compare favourably with related experiments, although they demonstrate the complexity of this challenging task.

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Section: Content-based Recommender Systemsmentioning

confidence: 99%

Section: Content-based Recommender Systemsmentioning

confidence: 99%

TWIN: Personality-based Intelligent Recommender System

Roshchina

Cardiff

Rosso

2015

IFS

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“…The method develops a utility function based on the decision maker's preference structure, and the utility function is used to find optimal solution (Sanayei, Mousavi, Abdi, & Mohaghar, 2008). Huang (2011) states that MAUT is a quantitative method which has an orderly process to identify and analyse multiple variables to find a solution. By applying the developed MAU (multi-attribute utility) function a decision maker can find the utility of every alternative, to identify the alternative with highest utility to select.…”

Section: Multi-attribute Utility Theorymentioning

confidence: 99%

“…Many different utility elicitation methods are developed to find decision maker's MAU function, which can be a holistic approach such as a multiple regression analysis or a decomposed approach (Huang, 2011). Sensitivity analysis is a part of MAUT procedure (Min, 1994).…”

Section: Is As Followsmentioning

confidence: 99%

Identifying Operational Requirements to Select Suitable Decision Models for a Public Sector E-Procurement Decision Support System

Adil

Nunes

Guo

2014

JISTEM

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Public sector procurement should be a transparent and fair process. Strict legal requirements are enforced on public sector procurement to make it a standardised process. To make fair decisions on selecting suppliers, a practical method which adheres to legal requirements is important. The research that is the base for this paper aimed at identifying a suitable Multi-Criteria Decision Analysis (MCDA) method for the specific legal and functional needs of the Maldivian Public Sector. To identify such operational requirements, a set of focus group interviews were conducted in the Maldives with public officials responsible for procurement decision making. Based on the operational requirements identified through focus groups, criteria-based evaluation is done on published MCDA methods to identify the suitable methods for e-procurement decision making. This paper describes the identification of the operational requirements and the results of the evaluation to select suitable decision models for the Maldivian context.

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“…The Utility-based recommender systems make recommendations based on the computation of the utility of each item for the user. Utility-based recommendation techniques use features of items as background data, adduce utility functions over items for users to describe user liking, and apply the function to determine the rank of items for a user [22].…”

Section: Related Workmentioning

confidence: 99%

A Literature Review of Empirical Studies of Recommendation Systems

Akhtar¹,

Agarwal²

2015

IJAIS

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In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in ecommerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user's needs and preferences, and UtilityBased Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.

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Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods

Cited by 94 publications

References 43 publications

TWIN: Personality-based Intelligent Recommender System

TWIN: Personality-based Intelligent Recommender System

Identifying Operational Requirements to Select Suitable Decision Models for a Public Sector E-Procurement Decision Support System

A Literature Review of Empirical Studies of Recommendation Systems

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