DCFLA: A distributed collaborative-filtering neighbor-locating algorithm

Xie, Bin; Han, Peng; Yang, Fan; Shen, Ruimin; Zeng, Hua-Jun; Chen, Zheng

doi:10.1016/j.ins.2006.09.005

Cited by 41 publications

(21 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most successful methods is collaborative filtering (CF) that recommends products based on the similarity of users in online stores that is calculated using users' ratings on items. CF has been proved to be successful by numerous studies and has been implemented by many real-world businesses [4,6,8,9,14,15,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

Ahn

2008

Information Sciences

621

313

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

Ahn

2008

Information Sciences

621

313

View full text Add to dashboard Cite

“…in a CF based website) in such a way that when enough information is stored on the system, we can make recommendations to each user based on information provided by those users we consider to have the most in common with them. CF is an interesting open research field (Xie, Han, Yang, Shen, Zeng, & Chen, 2007;Bobadilla, Hernando, Ortega, & Bernal, 2011;Bobadilla, Hernando, Ortega, & Gutiérrez, 2012). As noted earlier, user ratings can also be implicitly acquired (e.g., number of times a song is heard, information consulted and access to a resource).…”

Section: Content-based Filteringmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

View full text Add to dashboard Cite

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,

show abstract

“…In [16] and [34], a distributed collaborative filtering (DCF) algorithm DCFLA is introduced. The scalability of the system is guaranteed by distributing a user-profile management scheme using distributed hash table-based routing algorithms.…”

Section: Distributed Systemsmentioning

confidence: 99%

In-memory, distributed content-based recommender system

et al. 2013

View full text Add to dashboard Cite

Burdened by their popularity, recommender systems increasingly take on larger datasets while they are expected to deliver high quality results within reasonable time. To meet these ever growing requirements, industrial recommender systems often turn to parallel hardware and distributed computing. While the MapReduce paradigm is generally accepted for massive parallel data processing, it often entails complex algorithm reorganization and suboptimal efficiency because mid-computation values are typically read from and written to hard disk. This work implements an in-memory, content-based recommendation algorithm and shows how it can be parallelized and efficiently distributed across many homogeneous machines in a distributed-memory environment. By focusing on data parallelism and carefully constructing the definition of work in the context of recommender systems, we are able to partition the complete calculation process into any number of independent and equally sized jobs. An empirically validated performance model is developed to predict parallel speedup and promises high efficiencies for realistic hardware configurations. For the MovieLens 10M dataset we note efficiency values up to 71% for a configuration of 200 computing nodes (8 cores per node).

show abstract

DCFLA: A distributed collaborative-filtering neighbor-locating algorithm

Cited by 41 publications

References 21 publications

A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

Incorporating group recommendations to recommender systems: Alternatives and performance

In-memory, distributed content-based recommender system

Contact Info

Product

Resources

About