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• W2105373246 abstract "The purpose of recommender systems is to filter information unseen by a user to predict whether a user would like a given item. Making effective recommendations from a domain consisting of millions of ratings is a major research challenge in the application of machine learning and data mining. A number of approaches have been proposed to solvethe recommendation problem, where the main motivation is to increase the accuracy of the recommendations while ignoring other design objectives such as scalability, sparsity and imbalanced dataset problems, cold-start problems, and long tail problems. The aim of this thesis is to develop recommendation algorithms that satisfy the aforementioned design objectives making the recommendation generation techniques applicable to a wider range of practical situations and real-world scenarios. With this in mind, in the first half of the thesis, we propose novel hybrid recommendation algorithms that give accurate results and eliminate some of the known problems with recommender systems. More specifically, we propose a novel switching hybrid recommendation framework that combines Collaborative Filtering (CF) with a content-based filtering algorithm. Our experiments show that the performance of our algorithm is better than (or comparable to) the other hybrid recommendation approaches available in the literature. While reducing the dimensions of the dataset by Singular Value Decomposition (SVD), prior to applying CF, we discover that the SVD-based CF fails to produce reliable recommendations for some datasets. After further investigation, we fi?nd out that the SVD-based recommendations depend on the imputation methods used to approximate the missing values in the user-item rating matrix. We propose various missing value imputation methods, which exhibit much superior accuracy and performance compared to the traditional missing value imputation method - item average. Furthermore, we show how the gray-sheep users problem associated with a recommender systemcan effectively be solved using the K-means clustering algorithm. After analysing the effect of different centroid selection approaches and distance measures in the K-means clustering algorithm, we demonstrate how the gray-sheep users in a recommender system can be identified by treating them as an outlier problem. We demonstrate that the performance (accuracy and coverage) of the CF-based algorithms suffers in the case of gray-sheep users. We propose a hybrid recommendation algorithm to solve the gray-sheep users problem. In the second half of the thesis, we propose a new class of kernel mapping recommender system methods that we call KMR for solving the recommendation problem. The proposed methods find the multi-linear mapping between two vector spaces based on the structure-learning technique. We propose the user- and item-based versions of the KMR algorithms and offer various ways to combine them. We report results of an extensive evaluation conducted on five different datasets under various recommendation conditions. Our empirical study shows that the proposed algorithms offer a state-of-the-art performance and provide robust performance under all conditions. Furthermore, our algorithms are quite flexible as they can incorporate more information|ratings, demographics, features, and contextual information|easily into the forms of kernels and moreover, these kernels can be added/multiplied. We then adapt the KMR algorithm to incorporate new data incrementally. We offer a new heuristic namely KMRincr that can build the model without retraining the whole model from scratch when new data are added to the recommender system, providing significant computation savings. Our final contribution involves adapting the KMR algorithms to build the model on-line. More specifically, we propose a perceptron-type algorithm namely KMR percept which is a novel, fast, on-line algorithm for building the model that maintains good accuracy and scales well with the data. We provide the temporal analysis of the KMR percept algorithm. The empirical results reveal that the performance of the KMR percept is comparable to the KMR, and furthermore, it overcomes some of the conventional problems with recommender systems." @default.
• W2105373246 created "2016-06-24" @default.
• W2105373246 creator A5063552634 @default.
• W2105373246 date "2012-05-01" @default.
• W2105373246 modified "2023-09-23" @default.
• W2105373246 title "Robust, scalable, and practical algorithms for recommender systems" @default.
• W2105373246 cites W1479822238 @default.
• W2105373246 cites W1501500081 @default.
• W2105373246 cites W1519136899 @default.
• W2105373246 cites W1519470297 @default.
• W2105373246 cites W1526740443 @default.
• W2105373246 cites W154197214 @default.
• W2105373246 cites W1561317163 @default.
• W2105373246 cites W1563088657 @default.
• W2105373246 cites W1570184525 @default.
• W2105373246 cites W1580421455 @default.
• W2105373246 cites W1582340466 @default.
• W2105373246 cites W1598277080 @default.
• W2105373246 cites W1603079404 @default.
• W2105373246 cites W1651093245 @default.
• W2105373246 cites W1652319903 @default.
• W2105373246 cites W166522738 @default.
• W2105373246 cites W1678393259 @default.
• W2105373246 cites W177624111 @default.
• W2105373246 cites W1789957497 @default.
• W2105373246 cites W1832221731 @default.
• W2105373246 cites W1852540207 @default.
• W2105373246 cites W1966553486 @default.
• W2105373246 cites W1967921672 @default.
• W2105373246 cites W1976618413 @default.
• W2105373246 cites W1978044639 @default.
• W2105373246 cites W1988970025 @default.
• W2105373246 cites W1990846291 @default.
• W2105373246 cites W1994389483 @default.
• W2105373246 cites W1996283866 @default.
• W2105373246 cites W1999047234 @default.
• W2105373246 cites W1999062773 @default.
• W2105373246 cites W2005064532 @default.
• W2105373246 cites W2011430131 @default.
• W2105373246 cites W2018597607 @default.
• W2105373246 cites W2021680564 @default.
• W2105373246 cites W2023954349 @default.
• W2105373246 cites W2028036481 @default.
• W2105373246 cites W2031456732 @default.
• W2105373246 cites W2035720976 @default.
• W2105373246 cites W2039717455 @default.
• W2105373246 cites W2054141820 @default.
• W2105373246 cites W2060772621 @default.
• W2105373246 cites W2061988723 @default.
• W2105373246 cites W2064173066 @default.
• W2105373246 cites W2066590388 @default.
• W2105373246 cites W2072773380 @default.
• W2105373246 cites W2073405229 @default.
• W2105373246 cites W2073459066 @default.
• W2105373246 cites W2091812231 @default.
• W2105373246 cites W2101843930 @default.
• W2105373246 cites W2102457013 @default.
• W2105373246 cites W2106257764 @default.
• W2105373246 cites W2109943925 @default.
• W2105373246 cites W2109992782 @default.
• W2105373246 cites W2110325612 @default.
• W2105373246 cites W2118079529 @default.
• W2105373246 cites W2122090912 @default.
• W2105373246 cites W2123427850 @default.
• W2105373246 cites W2123504579 @default.
• W2105373246 cites W2124029832 @default.
• W2105373246 cites W2124133956 @default.
• W2105373246 cites W2126026531 @default.
• W2105373246 cites W2127480961 @default.
• W2105373246 cites W2132708887 @default.
• W2105373246 cites W2133099110 @default.
• W2105373246 cites W2133544219 @default.
• W2105373246 cites W2133790168 @default.
• W2105373246 cites W2133990480 @default.
• W2105373246 cites W2137245235 @default.
• W2105373246 cites W2139212933 @default.
• W2105373246 cites W2147152072 @default.
• W2105373246 cites W2149684865 @default.
• W2105373246 cites W2153635508 @default.
• W2105373246 cites W2154986869 @default.
• W2105373246 cites W2155106456 @default.
• W2105373246 cites W2156249918 @default.
• W2105373246 cites W2158515176 @default.
• W2105373246 cites W2165395308 @default.
• W2105373246 cites W2167598575 @default.
• W2105373246 cites W2168100051 @default.
• W2105373246 cites W2169305649 @default.
• W2105373246 cites W2170551306 @default.
• W2105373246 cites W2171960770 @default.
• W2105373246 cites W2172248814 @default.
• W2105373246 cites W2172249709 @default.
• W2105373246 cites W2248814772 @default.
• W2105373246 cites W2295995046 @default.
• W2105373246 cites W2596118091 @default.
• W2105373246 cites W2798766386 @default.
• W2105373246 cites W281665770 @default.
• W2105373246 cites W3029645440 @default.
• W2105373246 cites W54764438 @default.
• W2105373246 cites W628035 @default.
• W2105373246 cites W80868611 @default.

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