Session  6  Thuesday 25, June

 

 SoNARS: a Social Networks-based Algorithm for Social Recommender Systems

Francesca Carmagnola, Fabiana Vernero, Pierluigi Grillo

Abstract: User modeling systems have been influenced by the overspread of Web 2.0 and social networks. New systems aimed at helping people finding information of interest and including ``social functions'' like social networks, tagging, commenting, inserting content, arose. Such systems are the so-called ``social recommender systems''. The idea at the base of social recommender systems is that the recommendation of content should follow user's preferences while social network just represents a group of users joined by some kind of volunteered relation and does not reflect any preference. We claim that social network is a very important source of information to profile users. Moving from theories in social psychology which describe influence dynamics among individuals, we state that joining in a network with other people exposes individuals to social dynamics which can influence their attitudes, behaviours and preferences. We present in this paper SoNARS, a new algorithm for recommending content in social recommender systems. SoNARS targets users as members of social networks, suggesting items that reflect the trend of the network itself, based on its structure and on the influence relationships among users.
 Grocery Product Recommendations from Natural Language Inputs

Petteri Nurmi, Andreas Forsblom, Patrik Floreen

Abstract: Shopping lists play a central role in grocery shopping. Among other things, shopping lists serve as memory aids and as a tool for budgeting. More interestingly, shopping lists serve as an expression and indication of customer needs and interests. Accordingly, shopping lists can be used as an input for recommendation techniques. In this paper we describe a methodology for making recommendations about additional products to purchase using items on the user's shopping list. As shopping list entries seldom correspond to products, we first use information retrieval techniques to map the shopping list entries into candidate products. Association rules are used to generate recommendations based on the candidate products.  We evaluate the usefulness and interestingness of the recommendations in a user study.
 I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems

Xavier Amatriain, Josep M. Pujol, Nuria Oliver

 Abstract: Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth. However, users could be inconsistent in giving their feedback. Therefore, they would introduce an unknown amount of noise that could be challenging the validity of this assumption. In this paper, we tackle the problem of analyzing and characterizing noise inuser feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We show that this user generated noise is close to the error obtained by current state-of-the-art recommendation algorithms. We also analyze how factors such as item sorting and time of presentation affect this noise.
 Evaluating Interface Variants on Personality Acquisition for Recommender Systems

Greg Dunn, Jurgen Wiersema, Jaap Ham, Lora Aroyo

Abstract: Recommender systems help users find personally relevant media content in response to an overwhelming amount of this content available digitally. A prominent issue with recommender systems is recommending new content to new users; commonly referred to as the cold start problem. It has been argued that detailed user characteristics, like personality, could be used to mitigate cold start. To explore this solution, three alternative methods measuring userspersonality were compared to investigate which would be most suitable for user information acquisition. Participants (N = 60) provided user ease of use and satisfaction ratings to evaluate three different interface variants believed to measure participants personality characteristics. Results indicated that the NEO interface and the CFG interface were promising methods for measuring personality. Results are discussed in terms of potential benefits and broader implications for recommender systems.
 Context-dependent Personalised Feedback Prioritisation in Exploratory Learning for Mathematical Generalisation

Mihaela Cocea, George Magoulas

Abstract: In this paper we address the problem of prioritising the feedback on the basis of multiple heterogeneous pieces of information in exploratory learning. The problem arises when multiple types of feedback are required in order to address different types of conceptual difficulties, accommodate particular learning behaviours identified during exploration, and provide appropriate support depending on the learning mode (e.g. individual or collaborative learning) and/or the stage of the exploratory learning process. We propose an approach that integrates learners' characteristics and context-related information through a Multicriteria Decision-Making formalism. The outcome is a context-aware mechanism for prioritising personalised feedback that is tested in an exploratory learning environment for mathematical generalisation.