Matrix Factorization Techniques for Recommender Systems

Modern Graph Theory

Framing Public Life: Perspectives on Media and Our Understanding of the Social World. Stephen D. Reese, Oscar H. Gandy Jr., and August E. Grant, eds. Mahwah, NJ: Lawrence Erlbaum Associates, 2001. 399 pp. $99.95 hbk. When I conduct content analyses of transcripts, media content, and government documents with students and other faculty, I often advocate combining the study of agenda setting with the study of framing. Especially when I will not be the one doing the work. Conceptually, the pairing of issue salience with issue portrayal is a natural: just as the agenda-setting process can be studied as diffusion or social change involving institutional actors and nascent groups, through media attention, and on to the actions and nonactions of bureaucrats and elected officials, so too can framing provide a compelling and malleable lens through which to understand political jockeying and media access, issue portrayal in media, and the meanings assigned to media content by members of the public. But operationalization is another matter. Whereas the means of studying agenda setting are rather delimited (parsimony certainly accounted for part of its initial attractiveness to media scholars), approaches to framing analysis suggest a bit of a free-for-all. Especially when quantification of frames is attempted, I find the effort less than optimum in terms of what we usually learn. Even when we are trying not to, we tend to mistakenly weed out and discard the substance of frames (meanings, associations, metaphors, interests)in our pursuit of inter-coder reliability. I have found the study of framing to be more difficult and oftentimes more insightful than the study of agenda setting, and, when paired, a perfect example of the advantages of multi-method research designs. Framing Public Life is a very useful book for providing guidance about conceptual and operational alternatives and procedures concerning framing. Faced with a loose and complex paradigm of research, the editors have spent far more than the average amount of time pulling together themes across contributed chapters, carefully constructing prologues and epilogues, allowing time for contributors to react to the editors' own claims and positions about framing and the study of media, and commissioning chapters that were not originally presented at the 1997 University of South Carolina conference that served as the basis for this volume. The collection is just that: not a unified approach to framing, but rather a well-integrated and diverse set of perspectives on framing. Mapping out a citation analysis based on the references cited in these collected chapters would be interesting; I expect that while cliques of researchers focusing, for example, on social movements would be discernible, an overall network among framing scholars could be demonstrated. …

Framing Public Life: Perspectives on Media and Our Understanding of the Social World

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not only relevant and novel to the target user, but also significantly different from the items that the user has rated. However, the concept of serendipity is very subjective and serendipitous encounters are very rare in real-world scenarios, which makes serendipitous recommendations extremely difficult to study. To date, various definitions and evaluation metrics to measure serendipity have been proposed, and there is no wide consensus on which definition and evaluation metric to use. In this paper, we summarize most important approaches to serendipity in recommender systems, compare different definitions and formalizations of the concept, discuss serendipity-oriented recommendation algorithms and evaluation strategies to assess the algorithms, and provide future research directions based on the reviewed literature.

A survey of serendipity in recommender systems

Recommender systems are important tools that provide information items to users, by adapting to their characteristics and preferences. Usually items are recommended to individuals, but there are contexts in which people operate in groups. To support the recommendation process in social activities, group recommender systems were developed. Since different types of groups exist, group recommendation should adapt to them, managing heterogeneity of groups. This chapter will present a survey of the state-of-the-art in group recommendation, focusing on the type of group each system aims to. A new approach for group recommendation is also presented, able to adapt to technological constraints (e.g., bandwidth limitations), by automatically identifying groups of users with similar interests.

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State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups

Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness.

Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities

Recommender systems learn from historical users’ feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality.

Ludovico Boratto

Papers

State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups

Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities

Connecting user and item perspectives in popularity debiasing for collaborative recommendation

A multi-biometric system for continuous student authentication in e-learning platforms

Group Recommender Systems: An Introduction