A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

Date

2015-09-24

Author

Gurcan, Fatih
Birtürk, Ayşe Nur

Metadata

Show full item record

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

Item Usage Stats

166
views

0
downloads

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 compare CBCFdfc with pure content-based filtering (PCB), pure collaborative filtering (PCF) and content-boosted collaborative filtering (CBCF) according to prediction accuracy metrics, and with online CBCF without clustering (CBCFonl) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We also evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The user evaluation results are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.

Subject Keywords

Recommender systems, Hybrid online movie recommendation

URI

https://hdl.handle.net/11511/48571

DOI

https://doi.org/10.1007/978-3-319-22635-4_14

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

An Adjusted Recommendation List Size Approach for Users' Multiple Item Preferences Peker, Serhat; Koçyiğit, Altan (2016-09-10) 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...
A hybrid evolutionary performance improvement procedure for optimisation of continuous variable discharge concentrators Sakuhuni, Givemore; Klein, Bern; Altun, Naci Emre (2015-05-05) An iterative hybrid performance improvement approach integrating artificial neural network modelling and Pareto genetic algorithm optimisation was developed and tested. The optimisation procedure, code named NNREGA, was tested for tuning pilot scale Continuous Variable Discharge Concentrator (CVD) in order to simultaneously maximise recovery and upgrade ratio of gold bearing sulphides from a polymetallic massive sulphide ore. For the tests the CVD was retrofitted during normal operation on the flotation tai...
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...
A Dynamic Modularity Based Community Detection Algorithm for Large-scale Networks: DSLM Aktunc, Riza; Toroslu, İsmail Hakkı; Ozer, Mert; Davulcu, Hasan (2015-08-28) In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework ...
A Novel Neural Network Method for Direction of Arrival Estimation with Uniform Cylindrical 12-Element Microstrip Patch Array Caylar, Selcuk; Dural, Guelbin; Leblebicioğlu, Mehmet Kemal (2008-01-01) In this study a new neural network algorithm is proposed for real time multiple source tracking problem with cylindrical patch antenna array based on a previous v reported Modified Neural Multiple Source Tracking Algorithm(MN-MUST). The proposed algorithm, namely Cylindrical Microstrip Patch Array Modified Neural Multiple Source Tracking Algorithm (CMN-MUST) implements W-MUST algorithm on a cylindrical microsttip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip...

Citation Formats

F. Gurcan and A. N. Birtürk, “A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering,” 2015, vol. 363, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48571.