Communities, Collaboration, and Recommender Systems in Personalized Web Search

Smyth, Barry; Coyle, Maurice; Briggs, Peter

doi:10.1007/978-0-387-85820-3_18

Cited by 19 publications

(22 citation statements)

References 82 publications

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“…Similar to Collaborative Searching (Smyth et al, 2010), the design goal of CodeRAnts is to take advantage of similarity and repetition of queries performed by programmer communities as a source of recommendations.…”

Section: Coderants Methodsmentioning

confidence: 99%

“…Let = { 1 , 2 , … , } be a set of queries performed by programmers in the past and = { 1 , 2 , … , }, a set of snippets of code. In the same way that the collaborative searching method proposed by Smyth et al (2010), computes the matrix, the CodeRAnts method also computes the matrix , such that , corresponds to the amount of times that the snippet of code was retrieved when the query was used by a programmer in the past.…”

Section: Coderants Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CodeRAnts: A recommendation method based on collaborative searching and ant colonies, applied to reusing of open source code

Caicedo-Castro¹,

Duarte-Amaya²

2014

ing.inv.

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This paper presents CodeRAnts, a new recommendation method based on a collaborative searching technique and inspired on the ant colony metaphor. This method aims to fill the gap in the current state of the matter regarding recommender systems for software reuse, for which prior works present two problems. The first is that, recommender systems based on these works cannot learn from the collaboration of programmers and second, outcomes of assessments carried out on these systems present low precision measures and recall and in some of these systems, these metrics have not been evaluated. The work presented in this paper contributes a recommendation method, which solves these problems. Keywords:Recommender Systems on Software Engineering, recommendation method based on collaborative searching, software reuse, open source software, ant colony. RESUMENEste artículo presenta CodeRAnts: un nuevo método de recomendación basado en la técnica de búsqueda colaborativa e inspirada en la metáfora de la colonia de hormigas. Este método es propuesto con el objetivo de llenar el vacío en el estado del arte en cuanto a los sistemas de recomendación diseñados para reutilizar software, cuyos trabajos previos presentan dos problemas. El primero, es que los sistemas de recomendación basados en esos trabajos no pueden aprender de la colaboración de los programadores, y segundo, que los resultados de las pruebas realizados sobre estos sistemas presentan medidas bajas de precisión y remembranza, incluso, en algunos de estos sistemas no se hizo una evaluación de estas métricas. La contribución de este trabajo es un método de recomendación que resuelva dichos problemas.Palabras clave: Sistemas de recomendación para ingeniería de software, método de recomendación basado en la búsqueda colaborativa, reutilización de software, software de fuente abierta y colonia de hormigas.

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Section: Coderants Methodsmentioning

confidence: 99%

Section: Coderants Methodsmentioning

confidence: 99%

CodeRAnts: A recommendation method based on collaborative searching and ant colonies, applied to reusing of open source code

Caicedo-Castro¹,

Duarte-Amaya²

2014

ing.inv.

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“…The underlying logic is that considering the context of a search query renders more relevant search results. This context involves users long-term search history and inferred preferences, as well as users' most recent prior searches [22]. Personalization mechanisms cause users to get different results based on their prior behaviors.…”

Section: Search Personalization Mechanismsmentioning

confidence: 99%

Do Online Health Information Bubbles Exist? An Analysis of Personalized Google Search Results

Slechten¹,

Courtois²,

Coenen³

2017

Preprint

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Background: There is considerable debate focusing on the consequences of online health information, which is generally related to issues such as information quality and the necessary literacy to identify such information. However, the availability of information is a prerequisite to provoke positive outcomes. Search engines inherently prioritize information. In this study, we consider the nature of the health information that is prioritized by Google. Moreover, we consider the potential occurrence of health filter bubbles, due to algorithmic personalization.Such bubbles could restrict the availability of high-quality information, and amplify user selfconfirmation.Objective: The aim of this study was to examine the nature of the information sources that are retained as results to a standardized set of health-related Google search queries and to inquire the way in which these search results reflect personalization. Methods:We harvested the personal search results of 380 Google users on 16 standardized health-related search queries. In addition, we conducted a survey measuring socio-demographic and health behavior variables. To define the nature of the collected search results we undertook a content analysis of the collected search queries, coding entries for type of website, revenue model, information authorship, and source materials.Results: An initial Latent Class Analysis (LCA) of the unique search results, based on type of website, revenue model, information authorship, and source materials showed four clusters of information types: commercial health news (33%), health goods and services (30%), user contributions (21%), and health advocacy (16%). To identify personalization, we conducted a subsequent LCA of the entire set of search results per participant. Five patterns emerge, significantly differing for the information they are composed of (i.e., 11 to 41% difference). Still, tests of covariates did not reveal any significant relations (p < .05) with user characteristics (i.e., participants' gender, age, perception of current health, frequency of searching for online health information, and the age of their Google accounts). Conclusions:The results indicate that the overall majority of health information is commercial in nature, and often lacks proper information on authorship and source materials. Moreover, we find mild evidence for the personalization of health information. However, we were unable to isolate potential grounds for this personalization.Trial Registration: We received ethical clearance from the Social and Societal Ethics Committee (SMEC) at the KU Leuven (https://admin.kuleuven.be/raden/en/smec). The clearance is registered as G-2016 01 449.

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“…In information retrieval, even powerful search engines such as google or yahoo still use onesize-fits-all strategy (Smyth, Coyle et al 2011). Therefore there is ongoing research to improve the performance of these search engines.…”

Section: Information Retrieval Systems Vs Recommender Systemsmentioning

confidence: 99%

A New User Model based Interactive Product Retrieval Process for improved eBuying

Alahakoon

2015

AJIS

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When searching for items online there are three common problems that e-buyers may encounter; null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. In the past information retrieval systems or recommender systems were used as solutions. With information retrieval systems, too rigorous filtering based on the user query to reduce unmanageable number of items result in either null retrieval or filtering out the items users prefer. Recommender systems on the other hand do not provide sufficient opportunity for users to communicate their needs. As a solution, this paper introduces a novel method combining a user model with an interactive product retrieval process. The new layered user model has the potential of being applied across multiple product and service domains and is able to adapt to changing user preferences. The new product retrieval algorithm is integrated with the user model and is able to successfully address null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. The process is demonstrated using a bench mark dataset and a case study. Finally the Product retrieval process is evaluated using a set of guidelines to illustrate its suitability to current eBuying environments.

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Communities, Collaboration, and Recommender Systems in Personalized Web Search

Cited by 19 publications

References 82 publications

CodeRAnts: A recommendation method based on collaborative searching and ant colonies, applied to reusing of open source code

CodeRAnts: A recommendation method based on collaborative searching and ant colonies, applied to reusing of open source code

Do Online Health Information Bubbles Exist? An Analysis of Personalized Google Search Results

A New User Model based Interactive Product Retrieval Process for improved eBuying

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