PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

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Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...

AIRSpec is a platform consisting of several chemometric packages developed for analysis of Fourier transform infrared (FTIR) spectra of atmospheric aerosols. The packages are accessible through a browser-based interface, which also generates the necessary ...

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can lea ...

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

Recent advances in remote sensing and actuation technologies, coupled with the large reach of the internet, allowed for the emergence of applications such as cyber-physical labs. Cyber-physical labs are the digital and remotely-accessible equivalent of the ...

Interest in developing user-centered sensing technologies for personalized behavior change has gained significant momentum. However, very little research work has been done to understand issues relative to user readiness and adoption of the sensing technol ...

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection t ...

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

Web Search is increasingly entity centric; as a large fraction of common queries target specific entities, search results get progressively augmented with semi-structured and multimedia information about those entities. However, search over personal web br ...

We present the Personalized News (PEN) recommender systems framework, currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researc ...

The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with simi ...

Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complem ...

Social media platforms are created and exploited for various activities carried out individually or collaboratively and relying on different resources and tools. Social media platforms are inherently contextual; the context being defined as a specific acti ...

We present WhatsUp, a collaborative filtering system for disseminating news items in a large-scale dynamic setting with no central authority. WhatsUp constructs an implicit social network based on user profiles that express the opinions of users about the ...

Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typicall ...

Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with sim ...

This document introduces the TimeCloud Front End, a web-based interface for the TimeCloud platform that manages large-scale time series in the cloud. While the Back End is built upon scalable, fault-tolerant distributed systems as Hadoop and HBase and take ...

We introduce WaRR, a tool that records and replays with high ﬁdelity the interaction between users and modern web applications. WaRR consists of two independent components: the WaRR Recorder and the WaRR Replayer. The WaRR Recorder is embedded in a web bro ...

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

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PEN recsys: a Personalized News Recommender Systems Framework

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