Paper Session 5: Towards RecSys that Care

Date: Friday, Oct 5, 2018, 09:00-10:30
Location: Parq D/E/F
Chair: Bart Knijnenburg

  • SPORecommending Social-Interactive Games for Adults with Autism Spectrum Disorders (ASD)
    by Yiu-Kai D. Ng, Maria Soledad Pera

    Games play a significant role in modern society, since they affect people of all ages and all walks of life, whether it be socially or mentally, and have direct impacts on adults with autism. Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders characterized by qualitative impairments in social relatedness and interaction, as well as difficulties in acquiring and using communication and language abilities. Adults with ASD often find it difficult to express and recognize emotions which makes it hard for them to interact with others socially. We have designed new interactive and collaborative games for autistic adults and developed a novel strategy to recommend games to them. Using modern computer vision and graphics techniques, we (i) track the player’s speech rate, facial features, eye contact, audio communication, and emotional states, and (ii) foster their collaboration. These games are personalized and recommended to a user based on games interested to the user, besides the complexity of games at different levels according to the deficient level of the emotional understanding and social skills to which the user belongs. The objective of developing and recommending short-head (i.e., familiar) and long-tail (i.e., unfamiliar) games for adults with ASD is to enhance their necessary social interacting skills with peers so that they can live a normal life.

    Full text in ACM Digital Library

  • SPOSustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations
    by Sabina Tomkins, Steven Isley, Ben London, Lise Getoor

    We present an approach for jointly discovering sustainable products and sustainability-minded customers while making recommendations informed by these discoveries. Identifying sustainable products, and the customers who are interested in purchasing them, can improve customer satisfaction while also having a potentially large positive environmental impact. Finding sustainable products and evaluating their claims is a significant barrier facing sustainability-minded customers. Tools that reduce both these burdens are likely to boost the sale of sustainable products. However, it is difficult to determine the sustainability characteristics of these products — there are a variety of certifications and definitions of sustainability, and quality labeling requires input from domain experts. In this paper, we propose a flexible probabilistic framework that uses domain knowledge to identify sustainable products and customers, and uses these labels to predict customer purchases. We evaluate our approach on grocery items from the Amazon catalog. Our proposed approach outperforms established recommender system models in predicting future purchases while jointly inferring sustainability scores for customers and products.

    Full text in ACM Digital Library

  • LPEnhancing Structural Diversity in Social Networks by Recommending Weak Ties
    by Javier Sanz-Cruzado, Pablo Castells

    Contact recommendation has become a common functionality in online social platforms, and an established research topic in the social networks and recommender systems fields. Predicting and recommending links has been mainly addressed to date as an accuracy-targeting problem. In this paper we put forward a different perspective, considering that correctly predicted links may not be all equally valuable. Contact recommendation brings an opportunity to drive the structural evolution towards desirable properties of the network as a whole, beyond the sum of the isolated gains for the individual users to whom recommendations are delivered –global properties that we may want to assess and promote as explicit recommendation targets.

    In this perspective, we research the definition of relevant diversity metrics drawing from social network analysis concepts, and linking to prior diversity notions in recommender systems. In particular, we elaborate on the notion of weak tie recommendation as a means to enhance the structural diversity of networks. In order to show the signification of the proposed metrics, we report experiments with Twitter data illustrating how state of the art contact recommendation methods compare in terms of our metrics; we examine the tradeoff with accuracy, and we show that diverse link recommendations result in a corresponding diversity enhancement in the flow of information through the network, with potential implications in mitigating filter bubbles.

    Full text in ACM Digital Library

  • LPExploring Author Gender in Book Rating and Recommendation
    by Michael D. Ekstrand, Mucun Tian, Mohammed Imran R. Kazi, Hoda Mehrpouyan, Daniel Kluver

    Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.

    Full text in ACM Digital Library

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