1,479 research outputs found
Trust based collaborative filtering
k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful
algorithm supporting recommender systems, attempts to relieve the problem
of information overload by generating predicted ratings for items users have not
expressed their opinions about; to do so, each predicted rating is computed based
on ratings given by like-minded individuals. Like-mindedness, or similarity-based
recommendation, is the cause of a variety of problems that plague recommender
systems. An alternative view of the problem, based on trust, offers the potential to
address many of the previous limiations in CF. In this work we present a varation of
kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users
to learn who and how much to trust one another by evaluating the utility of the rating
information they receive. This method redefines the way CF is performed, and
while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms
the basic similarity-based methods in terms of prediction accuracy
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Personal recommendations in requirements engineering : the OpenReq approach
[Context & motivation] Requirements Engineering (RE) is considered as one of the most critical phases in software development but still many challenges remain open. [Problem] There is a growing trend of applying recommender systems to solve open RE challenges like requirements and stakeholder discovery; however, the existent proposals focus on specific RE tasks and do not give a general coverage for the RE process. [Principal ideas/results] In this research preview, we present the OpenReq approach to the development of intelligent recommendation and decision technologies that support different phases of RE in software projects. Specifically, we present the OpenReq part for personal recommendations for stakeholders. [Contribution] OpenReq aim is to improve and speed up RE processes, especially in large and distributed systemsPeer ReviewedPostprint (author's final draft
Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering
We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.algorithms;probabilistic latent semantic analysis;hybrid recommender systems;recommender systems
- …