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

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Community-based Recommendations on Twitter: Avoiding The Filter Bubble

International audienceDue to their success, social network platforms are considered today as a major communication mean. In order to increase user engagement, they rely on recommender systems to personalize individual experience by filtering messages according to user interest and/or neighborhood. However some recent results exhibit that this personalization of content might increase the echo chamber effect and create filter bubbles. These filter bubbles restrain the diversity of opinions regarding the recommended content. In this paper, we first realize a thorough study of communities on a large Twitter dataset to quantify how recommender systems affect users' behavior and create filter bubbles. Then we propose the Community Aware Model (CAM) to counter the impact of different recommender systems on information consumption. Our results show that filter bubbles concern up to 10% of users and our model based on similarities between communities enhance recommender systems

Accounting for Taste: Using profile similarity to improve recommender systems

Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers currently struggle to evaluate the appropriateness of recommendations offered. With collaborative filtering, recommendations are based on people's ratings of items. In this paper, we propose that the usefulness of recommender systems can be improved by including more information about recommenders. We conducted a laboratory online experiment with 100 participants simulating a movie recommender system to determine how familiarity of the recommender, profile similarity between decision-maker and recommender, and rating overlap with a particular recommender influence the choices of decision-makers in such a context. While familiarity in this experiment did not affect the participants' choices, profile similarity and rating overlap had a significant influence. These results help us understand the decision-making processes in an online context and form the basis for user-centered social recommender system design

The Role of Transparency and Feedback on the Behavioral Intention to Reuse a Recommender System

The problem of choosing the right product that will best fit the consumers’ tastes and preferences also extends even in electronic commerce. However, e-commerce was able to create a technological proxy for this social filtering process that is called online Recommender Systems (RS). RS act as personalized decision guides, aiding users in decisions on matters related to personal taste. It has the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RS has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user’s perspective. This study identified transparency and feedback as some of the possible ways to effectively evaluate recommender systems based from the users’ perspective. Thus, the goal of this research wants to focus on examining and identifying the roles of transparency and feedback in recommender systems and how it affect the user’s attitude towards the system