Benchmarking News Recommendations in a Living Lab

Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization

Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage; unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data

Social Machinery and Intelligence

Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. We argue that this type of autonomous social machines has provided a new paradigm for the design of intelligent systems marking a new phase in the field of AI. The consequences of this observation range from methodological, philosophical to ethical. On the one side, it emphasises the role of Human-Computer Interaction in the design of intelligent systems, while on the other side it draws attention to both the risks for a human being and those for a society relying on mechanisms that are not necessarily controllable. The difficulty by companies in regulating the spread of misinformation, as well as those by authorities to protect task-workers managed by a software infrastructure, could be just some of the effects of this technological paradigm

Giving and Following Recommendations on Video-on-Demand Services

This is an empirical paper about giving, receiving and following recommendations on Video-on-Demand (VoD) services, including results on gender-specific differences. Based upon a model for infor-mation behavior on VoD services, we applied an online survey and generated 1,258 valid question-naires from active VoD users. Participants receive recommendations from the systems once a week on average, but they follow them only occasionally. They give actively recommendations to other people sever-al times a month. Users do not receive recommenda-tions from other users as often as from the services (only several times a month); however, they follow those personal recommendations more often. The most important source for receiving personal rec-ommendations is face-to-face communication. Obvi-ously, VoD users follow personal recommendations from other people more than suggestions from algo-rithmically generated recommender systems. Besides, self-determined content selection following intrinsic motivation is important. The findings are of interest for research on digital and social media and for VoD services

Serendipitous News Discovery Increases News Consumption in News Recommender Systems

News recommender system users obtain news via incidental exposure to news and experience serendipity in the incidental news consumption. Serendipitous news discovery, the same as serendipity, refers to discovering unexpected and useful information unintentionally. Researchers suggest building serendipitous news recommender systems and increasing serendipitous news discovery to increase the diversity of the news consumption. However, the impacts of serendipitous news discovery on news consumption are uninvestigated, and rare research provides theoretical guidance to the serendipitous news recommender systems. The thesis investigated the impacts of serendipitous news discovery on news consumption with a serendipityrelated emotion, surprise, as a mediator and need for activation as a moderator. 463 participants recruited from Amazon MTurk completed the online survey-experiment. The findings suggest that surprise mediates the correlations between serendipitous news discovery and news consumption. Users who experience higher serendipitous news discovery indicate more positive attitudes on news consumption in the news recommender systems. The results also indicate the possibility that the lack of constant serendipitous news discovery may lead to the consumption of the news similar to the news that trigger serendipity. The research suggests that serendipitous news discovery increases news consumption, including news selection and reading