Anchoring and ordering NGS contig assemblies by population sequencing (POPSEQ)

Peer reviewe

Weighted content based methods for recommending connections in online social networks

Online Social Networks currently have an important role in the life of millions of active internet users. Cases like Twitter are of special attention since a lot of connections are made between people who never met before and with no need of reciprocation. For this reason it is important to find new ways to provide recommendations that may be of interest for users. Should these recommendations focus on the popularity, on the activity, location, common friends or content? Should recommendations be influenced by egocentric or global network metrics? This research is the first phase of an in-depth study of a large dataset based on Twitter which aims to answer the previous questions. Despite many studies based on global rankings, the authors believe that recommendations should mostly be based on the preferences made by users in their own networks. This stage of the study focuses on the popularity and activity of links as indicators to predict connections.For this end, the authors compute a weight for each of these features, which varies for each user. Each pair tested is accepted if it satisfies a minimum total weight. Results show a slight but important improvement in performance when using two features instead of one, the results gives an insight that if more features are considered more improvements in predictions will be found. The results of this paper can and should be accompanied with more research

Weighted content based methods for recommending connections in online social networks

Online Social Networks currently have an important role in the life of millions of active internet users. Cases like Twitter are of special attention since a lot of connections are made between people who never met before and with no need of reciprocation. For this reason it is important to find new ways to provide recommendations that may be of interest for users. Should these recommendations focus on the popularity, on the activity, location, common friends or content? Should recommendations be influenced by egocentric or global network metrics? This research is the first phase of an in-depth study of a large dataset based on Twitter which aims to answer the previous questions. Despite many studies based on global rankings, the authors believe that recommendations should mostly be based on the preferences made by users in their own networks. This stage of the study focuses on the popularity and activity of links as indicators to predict connections.For this end, the authors compute a weight for each of these features, which varies for each user. Each pair tested is accepted if it satisfies a minimum total weight. Results show a slight but important improvement in performance when using two features instead of one, the results gives an insight that if more features are considered more improvements in predictions will be found. The results of this paper can and should be accompanied with more research

Recommender Systems from an Industrial and Ethical Perspective

Over the recent years, a plethora of recommender systems (RS) have been proposed by academics. The degree of adoptability of these algorithms by industrial e-commerce platforms remains unclear. To get an insight into real-world recommendation engines, we survey more than 30 existing shopping cart solutions and compare the performance of popular recommendation algorithms on proprietary e-commerce datasets. Our results show that deployed systems rarely go beyond trivial "best seller" lists or very basic personalized recommendation algorithms, which nevertheless exhibit superior performance to more elaborate techniques both in our experiments and other related studies. We also perform chronological dataset splits to demonstrate the importance of preserving the sequence of events during evaluation, and the recency of events during training. The second part of our research is still ongoing and focuses on various ethical challenges that complicate the design of recommender systems. We believe that this direction of research remains mostly neglected despite its increasing impact on RS' quality and safety