19,265 research outputs found
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. A collaborative filtering
(CF) algorithm recommends items of interest to the target user by leveraging
the votes given by other similar users. In a standard CF framework, it is
assumed that the credibility of every voting user is exactly the same with
respect to the target user. This assumption is not satisfied and thus may lead
to misleading recommendations in many practical applications. A natural
countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take
account of the difference in the credibilities of the voting users when
performing CF. To this end, this paper presents a trust inference approach,
which can predict the implicit trust of the target user on every voting user
from a sparse explicit trust matrix. Then an improved CF algorithm termed
iTrace is proposed, which takes advantage of both the explicit and the
predicted implicit trust to provide recommendations with the CF framework. An
empirical evaluation on a public dataset demonstrates that the proposed
algorithm provides a significant improvement in recommendation quality in terms
of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Recommendation, collaboration and social search
This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology.
Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly.
We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements
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