A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction

Özbal, Gozde
Karaman, Hilal
Alpaslan, Ferda Nur
Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to get rid of it by means of a content-boosted collaborative filtering approach applied to a web-based movie recommendation system. The main motivation is to investigate whether further success can be obtained by combining 'local and global user similarity' and 'effective missing data prediction' approaches, which were previously introduced and proved to be successful separately. The present work improves these approaches by taking the content information of the movies into account during the item similarity calculations. The comparison of the proposed approach with the original methods was carried out using mean absolute error, and more accurate predictions were achieved.


A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local Global Similarity and Missing Data Prediction
Özbal, Gözde; Kahraman, Hilal; Alpaslan, Ferda Nur (2010-09-22)
Many recommender systems lack in accuracy when the data used throughout the recommendation process is sparse. Our study addresses this limitation by means of a content boosted collaborative filtering approach applied to the task of movie recommendation. We combine two different approaches previously proved to be successful individually and improve over them by processing the content information of movies, as confirmed by our empirical evaluation results.
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Although query log analysis provides crucial insights about Web users' search interests, conducting such analyses is almost impossible for some languages, as large-scale and public query logs are quite scarce. In this study, we first survey the existing query collections in Turkish and discuss their limitations. Next, we adopt a novel strategy to obtain a set of Turkish queries using the query autocompletion services from the four major search engines and provide the first large-scale analysis of Web querie...
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Cete, A. Ruhsen; Yuekselen, M. Adil; Kaynak, Uenver (Elsevier BV, 2008-01-01)
In this study, an efficient numerical method is proposed for unifying the structured and unstructured grid approaches for solving the potential flows. The new method, named as the "alternating cell directions implicit - ACDI", solves for the structured and unstructured grid configurations equally well. The new method in effect applies a line implicit method similar to the Line Gauss Seidel scheme for complex unstructured grids including mixed type quadrilateral and triangle cells. To this end, designated al...
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We present a PDE-constrained optimization algorithm which is designed for parallel scalability on distributed-memory architectures with thousands of cores. The method is based on a line-search interior-point algorithm for large-scale continuous optimization, it is matrix-free in that it does not require the factorization of derivative matrices. Instead, it uses a new parallel and robust iterative linear solver on distributed-memory architectures. We will show almost linear parallel scalability results for t...
Citation Formats
G. Özbal, H. Karaman, and F. N. Alpaslan, “A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction,” COMPUTER JOURNAL, pp. 1535–1546, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40438.