21,427 research outputs found
User study for generating personalized summary profiles
Abstract and only some features (e.g., faces, reportage, text, chorus, host, etc.
Machine Learning of User Profiles: Representational Issues
As more information becomes available electronically, tools for finding
information of interest to users becomes increasingly important. The goal of
the research described here is to build a system for generating comprehensible
user profiles that accurately capture user interest with minimum user
interaction. The research described here focuses on the importance of a
suitable generalization hierarchy and representation for learning profiles
which are predictively accurate and comprehensible. In our experiments we
evaluated both traditional features based on weighted term vectors as well as
subject features corresponding to categories which could be drawn from a
thesaurus. Our experiments, conducted in the context of a content-based
profiling system for on-line newspapers on the World Wide Web (the IDD News
Browser), demonstrate the importance of a generalization hierarchy and the
promise of combining natural language processing techniques with machine
learning (ML) to address an information retrieval (IR) problem.Comment: 6 page
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match usersā personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Translating verbose information needs into crisp search queries is a
phenomenon that is ubiquitous but hardly understood. Insights into this process
could be valuable in several applications, including synthesizing large
privacy-friendly query logs from public Web sources which are readily available
to the academic research community. In this work, we take a step towards
understanding query formulation by tapping into the rich potential of community
question answering (CQA) forums. Specifically, we sample natural language (NL)
questions spanning diverse themes from the Stack Exchange platform, and conduct
a large-scale conversion experiment where crowdworkers submit search queries
they would use when looking for equivalent information. We provide a careful
analysis of this data, accounting for possible sources of bias during
conversion, along with insights into user-specific linguistic patterns and
search behaviors. We release a dataset of 7,000 question-query pairs from this
study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape
Semantic user profiling techniques for personalised multimedia recommendation
Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture usersā evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the usersā interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme
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