28 research outputs found
Threshold model of cascades in temporal networks
Threshold models try to explain the consequences of social influence like the
spread of fads and opinions. Along with models of epidemics, they constitute a
major theoretical framework of social spreading processes. In threshold models
on static networks, an individual changes her state if a certain fraction of
her neighbors has done the same. When there are strong correlations in the
temporal aspects of contact patterns, it is useful to represent the system as a
temporal network. In such a system, not only contacts but also the time of the
contacts are represented explicitly. There is a consensus that bursty temporal
patterns slow down disease spreading. However, as we will see, this is not a
universal truth for threshold models. In this work, we propose an extension of
Watts' classic threshold model to temporal networks. We do this by assuming
that an agent is influenced by contacts which lie a certain time into the past.
I.e., the individuals are affected by contacts within a time window. In
addition to thresholds as the fraction of contacts, we also investigate the
number of contacts within the time window as a basis for influence. To
elucidate the model's behavior, we run the model on real and randomized
empirical contact datasets.Comment: 7 pages, 5 figures, 2 table
Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships
Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated\ud
by Collaborative Filtering Systems (CFSs). Traditional CFSs\ud
based on Matrix Factorization operate on the ratings provided\ud
by users and have been recently extended to incorporate\ud
demographic aspects such as age and gender. In this paper we\ud
propose to merge CF techniques based on Matrix Factorization\ud
and information regarding social friendships in order to\ud
provide users with more accurate suggestions and rankings\ud
on items of their interest. The proposed approach has been\ud
evaluated on a real-life online social network; the experimental\ud
results show an improvement against existing CF approaches.\ud
A detailed comparison with related literature is also presen
Movie Recommenadation System
Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item
From Bandits to Experts: A Tale of Domination and Independence
We consider the partial observability model for multi-armed bandits,
introduced by Mannor and Shamir. Our main result is a characterization of
regret in the directed observability model in terms of the dominating and
independence numbers of the observability graph. We also show that in the
undirected case, the learner can achieve optimal regret without even accessing
the observability graph before selecting an action. Both results are shown
using variants of the Exp3 algorithm operating on the observability graph in a
time-efficient manner
Targeted Advertising using Location
Advertising is the key factor of any social sites. The explosive growth of social networks increases the prolific availability in customer tastes and preferences. This data can be exploited to serve the customers better and offer them the advertisements to the customers. To provide relevant advertisements to consumers, its important to consider the location of the consumer as well. The consumers will be highly contented if the offers shown to them are easily accessible in nearby areas. we propose a model combining the idea of social and spatial data to provide targeted advertisements. Social data is acquired through user's Facebook profile and location of the user is found with the help of Beacons. In these we are also using the concept of GPS (Global Positioning System). GPS helps for providing the service globally, in which we can provide multiple services to multiple users. The GPS system operates independently of any internet reception, though these technologies can enhance the usefulness of the GPS positioning information
Evaluation and Assessment of Recommenders Using Monte Carlo Simulation
There have been various definitions, representations and derivations of trust in the context of recommender systems. This article presents a recommender predictive model based on collaborative filtering techniques that incorporate a fuzzy-driven quantifier, which includes two upmost relevant social phenomena parameters to address the vagueness inherent in the assessment of trust in social networks relationships. An experimental evaluation procedure utilizing a case study is conducted to analyze the overall predictive accuracy. These results show that the proposed methodology improves the performance of classical recommender approaches. Possible extensions are then outlined
May I Suggest? Comparing Three PLE Recommender Strategies
Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general