Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting empha...
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Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://***/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests to ensure code quality, but it is also accompanied with a detailed documentation, tutorials, examples, and several built-in benchmarking data sets.
Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest...
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Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them;hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.
Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. In this paper we p...
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Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. In this paper we present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal that these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield different outcomes, with the matrix factorization algorithm producing opinion polarization. Our approach shed light on the interplay between algorithmic recommendations and novelties discovery, offering a framework to enhance recommendation techniques beyond accuracy metrics.
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collab...
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Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.
In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in...
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In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in hyperbolic space with greedy routing, exploiting properties of hyperbolic metric spaces. Contrary to the existing recommender systems that rank products in order to propose the highest ranked ones to the users, our proposed recommender system creates a progressive path of recommendations towards a final (known or inferred) target product using greedy routing over networks embedded in hyperbolic space. Thus, it prepares the user by intermediate recommendations for maximizing the chances that he/she accepts the recommendation of the target product(s). This casts the problem of locating a suitable recommendation as a path problem, where leveraging on the efficiency of greedy routing in graphs embedded in hyperbolic spaces and exploiting special network structure, if any, pays dividends. Two variants of our recommendation approach are provided, namely Hyperbolic recommendation-Known Destination (HRKD), Hyperbolic recommendation-Unknown Destination (HRUD), when the target product is known or unknown, respectively. We demonstrate how the proposed approach can be used for producing efficient recommendations in online systems, along with studying the impact of the several parameters involved in its performance via proper emulation of user activity over suitably defined graphs.
A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effectiv...
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ISBN:
(纸本)9781450324090
A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics from core algorithmic issues to the management of the human computer interaction.
Holding the greatest area of rainforest in the world, Brazil has seen the adoption of a far-right anti-environmental agenda under the administration of Jair Bolsonaro. This agenda was backed by a transnational infrast...
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Holding the greatest area of rainforest in the world, Brazil has seen the adoption of a far-right anti-environmental agenda under the administration of Jair Bolsonaro. This agenda was backed by a transnational infrastructure of right-wing media outlets on online platforms, including the conservative YouTube channel Brasil Paralelo. Our research attempted to understand how environmental conspiracies in Brasil Paralelo's documentary Cortina de Fumaca (in English, smokescreen) took advantage of both digital platform affordances and political alignment with the far-right government to gain social adherence and relevance. By carrying out topic modeling on more than 13,000 comments and network analysis of 982 recommended videos on YouTube, we aimed to analyze the following: (1) which narratives fostered in the documentary have reverberated among the audience that published comments on its YouTube page and (2) what type of video does YouTube recommend for users who watched Cortina de Fumaca. Our results show that far-right anti-environmental discourse is instrumentalized as yet another component of modern culture wars, where environmental conspiracies are placed side by side with other conspiratorial claims regarding politics, gender, religion, and other ideological subjects.
algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recomm...
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algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recommendations on YouTube to map the communities that the social network is creating through its algorithms and to test the network for homophily, that is, the connectedness between communities. We find that YouTube's channel recommendation algorithm fosters the creation of highly homophilous communities in the United States (n = 13,529 channels) and in Germany (n = 8,000 channels). Factors that seem to drive YouTube's recommendations are topics, language, and location. We highlight the issue of homophilous communities in the context of politics where YouTube's algorithms create far-right communities in both countries.
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