An algorithmic framework for performing collaborative filtering

Herlocker, Jonathan L.; Konstan, Joseph A.; Borchers, Al; Riedl, John

doi:10.1145/312624.312682

Cited by 1,793 publications

(642 citation statements)

References 17 publications

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“…When calculating the similarity between items in the traditional item-based CF technique, only the ratings given by the users to the products are used [11,12].The ratings which are given to items are presented in Tab. 1, in the form of a user-product matrix.…”

Section: Our Solutionmentioning

confidence: 99%

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users' preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising.

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Section: Our Solutionmentioning

confidence: 99%

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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“…Group Lens (Tornago 2006) originally employed various collaborative filtering algorithms (Breese et al 1998;Herlocker et al 1999) for predicting users' interests, based on explicitly provided users ratings, implicit ratings derived from users' navigation, and transaction histories (e.g., shopping basket operations, purchases). Group Lens stored all user ratings in a database, but kept a correlation matrix of all ratings in cache memory during runtime.…”

Section: Traditional Data Repositories Of Generic User Modeling Systementioning

confidence: 99%

“…It employs memory-based Spearman correlation for determining the proximity between users and various weighted prediction algorithms from the area of collaborative filtering (see Herlocker et al 1999). …”

Section: User Modeling Componentsmentioning

confidence: 99%

An LDAP-based User Modeling Server and its Evaluation

Kobsa

Fink

2006

User Model User-Adap Inter

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Representation components of user modeling servers have been traditionally based on simple file structures and database systems. We propose directory systems as an alternative, which offer numerous advantages over the more traditional approaches: international vendor-independent standardization, demonstrated performance and scalability, dynamic and transparent management of distributed information, built-in replication and synchronization, a rich number of pre-defined types of user information, and extensibility of the core representation language for new information types and for data types with associated semantics. Directories also allow for the virtual centralization of distributed user models and their selective centralized replication if better performance is needed. We present UMS, a user modeling server that is based on the Lightweight Directory Access Protocol (LDAP). UMS allows for the representation of different models (such as user and usage profiles, and system and service models), and for the attachment of arbitrary components that perform user modeling tasks upon these models. External clients such as user-adaptive applications can submit and retrieve information about users. We describe a simulation experiment to test the runtime performance of this server, and present a theory of how the parameters of such an experiment can be derived from empirical web usage research. The results show that the performance of UMS meets the requirements of current small and medium websites already on very modest hardware platforms, and those of very large websites in an entry-level business server configuration.

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“…Existem 2 grandes tipos de abordagens quando falamos no problema de recomendação: baseada em conteúdo [3] e filtragem colaborativa [10]. Os modelos baseados em conteúdo tentam aprender, como atributos de produtos e atributos de usuários interagem, para produzir um número que descreva alguma relação, como uma relação de preferência.…”

Section: Introductionunclassified

“…Ele possui uma estrutura de alto nível, que possibilitou em 1999, que Herlocker [10] propusesse um framework algoritmico de para filtragem colaborativa. Nesse framework, a principal operaçãoé o cálculo da similaridade entre dois usuários ou itens, e com esses valores de similaridade em mãos, fazer a prediçãoé apenas uma conta que pondera similaridades e notas.…”

Section: Introductionunclassified

Sistemas De Recomendação Utilizando Máquinas De Boltzmann Restritas

CRUZ¹

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Felipe João Pontes da Cruz; Milidiú Ruy Luiz(Advisor). Recommender systems using Restricted Boltzmann machines. Rio de Janeiro, 2016. 52p. MSc Thesis -Department of Informática, Pontifícia Universidade Católica do Rio de Janeiro.Recommender systems can be used in many problems in the real world. Many models were proposed to solve the problem of predicting missing entries in a specific dataset. Two of the most common approaches are neighborhood-based collaborative filtering and latent factor models. A more recent alternative was proposed on 2007 by Salakhutdinov, using Restricted Boltzmann Machines. This models belongs to the family of latent factor models, in which, we model latent factors over the data using hidden binary units. RBMs have shown that they can approximate solutions trained with a traditional matrix factorization model. In this work we'll revisit this proposed model and carefully detail how to model and train RBMs for the problem of missing ratings prediction.

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An algorithmic framework for performing collaborative filtering

Cited by 1,793 publications

References 17 publications

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

An LDAP-based User Modeling Server and its Evaluation

Sistemas De Recomendação Utilizando Máquinas De Boltzmann Restritas

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