An Adjusted Recommendation List Size Approach for Users' Multiple Item Preferences

Date

2016-09-10

Author

Peker, Serhat
Koçyiğit, Altan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

162
views

0
downloads

This paper describes the design and implementation of a novel approach to dynamically adjust the recommendation list size for multiple preferences of a user. By considering users' earlier preferences, machine learning techniques are employed to estimate the optimal recommendation list size according to current conditions of users. The proposed approach has been evaluated on real-life data from grocery shopping domain by conducting a series of experiments. The results show that the proposed approach achieves better overall recommendation quality than the standard approach and it outperforms the benchmark method in efficiency by shortening the recommendation list while maintaining the effectiveness.

Subject Keywords

Top-N recommender systems, Recommendation list size, Recommendation length, Recommendation quality, Recommendation efficiency

URI

https://hdl.handle.net/11511/31370

DOI

https://doi.org/10.1007/978-3-319-44748-3_30

Conference Name

17th International Conference on Artificial Intelligence - Methodology, Systems and Applications (AIMSA)

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering Gurcan, Fatih; Birtürk, Ayşe Nur (2015-09-24) We propose an online hybrid recommender strategy named content-boosted collaborative filtering with dynamic fuzzy clustering (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We com...
Determination of optimal product styles by ordinal logistic regression versus conjoint analysis for kitchen faucets Demirtas, Ezgi Aktar; ANAGÜN, AHMET SERMET; Köksal, Gülser (Elsevier BV, 2009-09-01) In this study, a two-stage integrated approach is proposed and implemented to explore user perceptions about kitchen faucet styles and to find optimal levels of design parameters related to product appearance. At the first stage, a group of representative users have been asked to judge 38 systematically selected different faucet designs by using a semantic differential (SD) scale for 11 image (kansei) words about their visual perceptions. Then the relations between overall preference and kansei word scores ...
A Hypergraph based framework for representing aggregated user profiles, employing it for a recommender system and personalized search through a hypernetwork method Tarakçı, Hilal; Manguoğlu, Murat; Çiçekli, Fehime Nihan; Department of Computer Engineering (2017) In this thesis, we present a hypergraph based user modeling framework to aggregate partial profiles of the individual and obtain a complete, semantically enriched, multi-domain user model. We also show that the constructed user model can be used to support different personalization services including recommendation. We evaluated the user model against datasets consisting of user's social accounts including Facebook, Twitter, LinkedIn and Stack Overflow. The evaluation results confirmed that the proposed use...
An interactive probabilistic approach to multi-criteria sorting BUGDACI, Asli Gul; KOKSALAN, Murat; Ozpeynirci, Selin; Serin, Yaşar Yasemin (2013-10-01) This article addresses the problem of sorting alternatives evaluated by multiple criteria among preference-ordered classes. An interactive probabilistic sorting approach is developed in which the probability of an alternative being in each class is calculated and alternatives are assigned to classes keeping the probability of incorrect assignments below a specified small threshold value. The decision maker is occasionally required to place alternatives to classes. The probabilities for unassigned alternativ...
An elitist self-adaptive step-size search for structural design optimization Azad, S. Kazemzadeh; Hasançebi, Oğuzhan (2014-06-01) This paper presents a method for optimal sizing of truss structures based on a refined self-adaptive step-size search (SASS) algorithm. An elitist self-adaptive step-size search (ESASS) algorithm is proposed wherein two approaches are considered for improving (i) convergence accuracy, and (ii) computational efficiency. In the first approach an additional randomness is incorporated into the sampling step of the technique to preserve exploration capability of the algorithm during the optimization. Furthermore...

Citation Formats

S. Peker and A. Koçyiğit, “An Adjusted Recommendation List Size Approach for Users’ Multiple Item Preferences,” Varna, BULGARIA, 2016, vol. 9883, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31370.