Open Source Software Recommendations Using Github

Koskela, Miika; Simola, Inka; Stefanidis, Kostas

doi:10.1007/978-3-030-00066-0_24

Cited by 18 publications

(15 citation statements)

References 6 publications

(4 reference statements)

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“…Typically, recommendation approaches [14] are distinguished between content-based, which recommends items similar to those the user previously preferred (see, e.g., [15]), and collaborative filtering, which recommend items that users with similar preferences like (see, e.g., [16]). Nowadays, recommendations have more broad applications [17], beyond products, like links (friends) recommendations [18], query recommendations [19], open source software recommendations [20], diverse venue recommendations [21], sequential recommendations [22,23], or even recommendations for evolution measures [24,25].…”

Section: Related Workmentioning

confidence: 99%

Multidimensional Group Recommendations in the Health Domain

2020

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Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents.

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Section: Related Workmentioning

confidence: 99%

Multidimensional Group Recommendations in the Health Domain

2020

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“…To facilitate users in their selection process, recommender systems provide suggestions on data items, which might be interesting for the respective users. Nowadays, recommendations have more broad applications, beyond products, like links (friends) recommendations [33], social-based recommendations [28], health-related recommendations [29], open source software recommendations [16], diverse venue recommendations [10], or even recommendations for evolution measures [27]. For achieving efficiency, there are approaches that build user models for computing recommendations.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Long Term Fairness in Recommendations with Variational Autoencoders

Borges

Stefanidis

2019

Proceedings of the 11th International Conference on Management of Digital EcoSystems

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Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term strategy. Variational Autoencoders (VAEs) were recently proposed as the state-of-the-art for collaborative filtering recommendations, but as every other approach, they generate homogeneous prediction scores among the highest positions. In this paper, we propose incorporating randomness in the regular operation of VAEs in order to increase the fairness (mitigate the position bias) in multiple rounds of recommendation. We argue that adding a noise component when sampling values from VAE's latent representation provides long term fairness, despite of a tolerable decrease in ranking quality (NDCG). We calculate the trade-off between unfairness and NDCG when introducing 4 different noise distributions. The solution has proved to be a very practical one and the results point for a clear positive effect of turning recommendation far more fair, despite some small NDCG loss in Movie Lens, Netflix and MSD datasets. In our best scenario, the unfairness was reduced by 76% despite a decrease of 5% in the quality of ranking. CCS CONCEPTS • Information systems → Recommender systems.

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“…Typically, depending on the type of the input data, i.e., user behavior, contextual information, item/user similarity, recommendation approaches are classified as content-based [8], collaborative filtering [9], knowledge-based [2], hybrid [1], or even social ones [12]. Nowadays, recommendations have more broad applications, beyond products, like links (friends) recommendations [15], social-based recommendations [12], query recommendations [3], health-related recommendations [13,14], open source software recommendations [6], diverse venue recommendations [4], recommendations for groups [7], or even recommendations for evolution measures [11].…”

Section: Related Workmentioning

confidence: 99%

“…Users were also asked to give a numerical interest score between 1 and 10 to each of the 11 players. This score was in the range [1,5], if the previous relevance indicator was 0 and in the range [6,10], otherwise. We reported the number of players that were rated highly (interest score ≥ 7), namely the Highly Preferred Players (HPP).…”

Section: Usability Evaluationmentioning

confidence: 99%

FIFARecs: A Recommender System for FIFA18

Klancar

Paulussen

Stefanidis

2019

Communications in Computer and Information Science

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One of the most popular features in the FIFA18 game is the career mode, where the target of the users is to improve their teams and win as much competitions as possible. Usually, it is hard for the users to decide which players to select to buy to maximally improve their team by taking into account all different players' attributes. In this paper, we introduce an approach towards helping the users to both determine the worst player in a specific team and recommend a list of players seen as possible replacement to improve the team.

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Open Source Software Recommendations Using Github

Cited by 18 publications

References 6 publications

Multidimensional Group Recommendations in the Health Domain

Multidimensional Group Recommendations in the Health Domain

Enhancing Long Term Fairness in Recommendations with Variational Autoencoders

FIFARecs: A Recommender System for FIFA18

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