The collaborative filtering recommendation based on SOM cluster-indexing CBR

Roh, Tae Hyup

doi:10.1016/s0957-4174(03)00067-8

Cited by 138 publications

(53 citation statements)

References 25 publications

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“…Among the most widely used models we have bayesian classifiers (Park, Hong, & Cho, 2007), bio-inspired networks (Roh, Oh, & Han, 2003;Merve-Acilar & Arslan, 2009), fuzzy systems (Yager, 2003;Nilashi, Ibrahim, & Ithnin, 2014), genetic algorithms (Ho, Fong, & Yan, 2007;Gao & Li, 2008), clustering methods (Wu, Chang, & Liu, 2014), latent features (Zhong & Li, 2010), matrix factorization (Lou, Xia, & Zhu, 2012), and probabilistic models (Mnih & Salakhutdinov, 2007;Wang & Blei, 2011), among others.…”

Section: Model-based Methodsmentioning

confidence: 99%

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Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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Section: Model-based Methodsmentioning

confidence: 99%

“…Two alternative NN uses are presented in Huang, Chuang, Ke, and Sandnes (2008) and Roh, Oh, & Han (2003). In the first case paper, authors use a training backpropagation NN for generating association rules that are mined from a transactional database.…”

Section: Bio-inspired Rsmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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“…Two alternative NN uses are presented in [20,45]. In the first case the strategy is based on training a back-propagation NN with association rules that are mined from a transactional datábase; in the second case they propose a model that combines a CF algorithm with two machine learning processes: SOM and Case Based Reasoning (CBR) by changing an unsupervised clustering problem into a supervised user preference reasoning problem.…”

Section: Neural Network Applied To Recommender Systemsmentioning

confidence: 99%

“…In [29] first, all users are segmented by demographic characteristics and users in each segment are clustered according to the preference of Ítems using the Self-Organizing Map (SOM) NN. Kohonon's SOMs are a type of unsupervised learning; their goal is to discover some underlying structure of the data.Two alternative NN uses are presented in [20,45]. In the first case the strategy is based on training a back-propagation NN with association rules that are mined from a transactional datábase; in the second case they propose a model that combines a CF algorithm with two machine learning processes: SOM and Case Based Reasoning (CBR) by changing an unsupervised clustering problem into a supervised user preference reasoning problem.…”

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confidence: 99%

A collaborative filtering approach to mitigate the new user cold start problem

Bobadilla

Ortega

Hernando

et al. 2012

Knowledge-Based Systems

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In order to measure the quality of the results of a RS, there is a wide range of metrics which are used to evalúate both the prediction and recommendation quality of these systems [17,1,18,6].CF based RS estímate the valué of an item not voted by a user via the ratings made on that item by a set of similar users. The overall quality in the prediction is called accuracy [3] and the mean absolute error (MAE) is normally used to obtain it [17]. The system's ability to make estimations is called coverage and it indicates the percentage of prediction which we can make using the set of similar users selected (usually, the more similar users we select and the more votes the selected users have cast, the better the coverage we achieve). In RS, besides aiming to improve the quality measures of the predictions (accuracy and coverage), there are other issues that need be taken into account [56,41,51]: avoiding overspecialization phenomena, finding good Ítems, credibility of recommendations, precisión and recall measures, etc.The rest of the paper is divided into the following sections (with the same numbering shown here):2. State of the art, in which a review is made of the most relevant contributions that exist in the CF aspects covered in the paper: cold-start and application of neural networks to the RS. 3. General hypothesis and motivations: what we aim to contribute and the indications that lead us to believe that carrying out research into this subject will provide satisfactory results that support the hypothesis set out. 4. Design of the user cold-start similarity measure: explanation and formalization of the design of the similarity measure proposed as a linear combination of simple similarity measures, by adjusting the weights using optimization techniques based on neural networks. 5. Collaborative filtering specifications: formalization of the CF methodology which specifies the way to predict and recommend, as well as to obtain the quality valúes of the predictions and recommendations. This is the formalization that supports the design of experiments carried out in the paper. The methodology is provided which describes the use of leave-one-out cross validation applied to obtaining the MAE, coverage, precisión and recall. 6. Design of the experiments with which the quality results are obtained provided by the user cold-star similarity measure proposed and by a set of current similarity measures for which we aim to improve the results. We use the Netflix (http:// www.netflixprize.com) and Movielens (http://www.movielens.org) databases. 7. Graphical results obtained in the experiments, complemented with explanations of the behavior of each quality measure. 8. Most relevant conclusions obtained. State of the art The cold-start issueThe cold-start problem [48,1] occurs when it is not possible to make reliable recommendations due to an initial lack of ratings. We can distinguish three kinds of cold-start problems: new community, new item and new user. The last kind is the most important in RS that are already in operat...

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“…SOM has proven to be a effective way not only to organize information, but also to visualize it, and even to allow content addressable searches (Vesanto and Alhoniemi, 2000;Dittenbach et al, 2000;Russell et al, 2002;Perelomov et al, 2002;Roh et al, 2003;Jieh-Haur and Chen, 2012;Barrón-Adame et al, 2012). …”

Section: The Self-organizing Map (Som)mentioning

confidence: 99%

Reorganizing clouds: A study on tag clustering and evaluation

García-Plaza

Zubiaga

Fresno

et al. 2012

Expert Systems with Applications

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Finding and visualizing semantic relations among tags within a tag cloud enhances user experience, particularly regarding access to and retrieval of web pages on social tagging systems. Several approaches have been proposed to visualize tag relations in these systems. However, results of previous research rely on qualitative evaluation methods, and do not provide robust and sound comparison criteria. In order to allow quantitative evaluation we present a benchmark social tagging dataset, where a subset of 140 tags from a well-known social bookmarking site, Delicious, have been manually categorized according to the Open Directory Project (ODP). The manual categorization is utilized as a ground truth that enables quantitative evaluation providing a way of inferring the best of different clustering approaches. With this dataset we also explore different tag representation approaches to present a reorganized tag cloud by using Self Organizing Maps. In addition, we present an approach to enrich the resultant tag cloud with the most characteristic terms for each tag and group of tags, making possible a further filtered navigation, both by tag and document content, and easing a deeper qualitative evaluation of the clusters.

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The collaborative filtering recommendation based on SOM cluster-indexing CBR

Cited by 138 publications

References 25 publications

Incorporating group recommendations to recommender systems: Alternatives and performance

Incorporating group recommendations to recommender systems: Alternatives and performance

A collaborative filtering approach to mitigate the new user cold start problem

Reorganizing clouds: A study on tag clustering and evaluation

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