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Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
TLDR
This work introduces a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User- Item matrix. Expand
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
TLDR
This paper proposes a new CF approach, Collaborative Less-is-More Filtering (CLiMF), where the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for measuring the performance of top-k recommendations. Expand
TFMAP: optimizing MAP for top-n context-aware recommendation
TLDR
This paper proposes TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context, and presents a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency, and to ensure its scalability. Expand
Didn't you see my message?: predicting attentiveness to mobile instant messages
TLDR
It is identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. Expand
When attention is not scarce - detecting boredom from mobile phone usage
TLDR
It is shown that a user-independent machine-learning model of boredom --leveraging features related to recency of communication, usage intensity, time of day, and demographics-- can infer boredom with an accuracy of up to 82.9%. Expand
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
TLDR
The findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. Expand
Understanding mobile web and mobile search use in today's dynamic mobile landscape
TLDR
The results of an online diary and interview study of 18 active mobile Web users over a 4-week period focusing on how, why, where and in what situations people use the mobile Internet and mobile search are presented. Expand
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
TLDR
While Frapp e performs very well based on usage-centric evaluation metrics insights from the small-scale study reveal some negative user experiences, these results point to a number of actionable lessons learned specically related to designing, deploying and evaluating mobile context-aware RS in-thewild with real users. Expand
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
TLDR
This paper presents a user study aimed at quantifying the noise in user ratings that is due to inconsistencies, and analyzes how factors such as item sorting and time of rating affect this noise. Expand
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
TLDR
This work presents a novel method for recommending items to users based on expert opinions, which is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the users. Expand
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