2,375 research outputs found
Personalized Recommendations on Twitter based on Explicit User Relationship Modelling
Information overload is a recent phenomenon caused by a regular use of social media platforms among millions of users. Websites such as Twitter seem to be getting increasingly popular, providing a perfect platform for sharing information which can help in the process of modelling users and recommender system research. This research studies information overload and uses twitter user modelling through making use of explicit relationships amongst various users. This paper presents a novel personal profile mechanism that helps in the provision of more accurate recommendations by filtering overloaded information as it gathered from Twitter data. The presented method takes advantage of user explicit relationships on Twitter based on influence rule in order to gain information which is vital in the building of the personal profile of the user. In order to validate this proposed method\u27s usefulness a simple tweet recommendation service was implemented by using content-based recommender system. This has also been evaluated using an offline evaluation process. Our proposed user profiles are compared against other profiles such as the baseline in order to have the proposed method\u27s effectiveness checked. The experiment is implemented based on an experimental number of users
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Recommender Systems for Online and Mobile Social Networks: A survey
Recommender Systems (RS) currently represent a fundamental tool in online
services, especially with the advent of Online Social Networks (OSN). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area
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