Field evaluation of context aware adaptive interfaces for efficient mobile contact retrieval

Text and phone calls: user behaviour and dual-channel communication prediction

“…In contrast, small data is often structured, easy to access, and easy to manage. Hence, small data has been successfully used to solve problems surrounding individuals like impact of communication technologies on relationships [24,25], communication behaviour of smartphone users [10,[13][14][15], user-centric context aware models for prediction [26], and call prediction [2,11].…”

“…Adaptive interfaces work by first predicting the k most likely alters at a given time and then presenting them to the user. Experimental results show that the adaptive interface offers faster contact retrieval in case of correct prediction and even in the case of prediction failure, there is a delay of 2-3 seconds [2].…”

“…Various variations of call prediction algorithms using call logs have been proposed with varying effectiveness [16,[18][19][20][36][37][38]. One of the more established algorithms is proposed by Stefanis et al [2,17] which uses recency and frequency of communication as contextual information and achieved a prediction accuracy of 80%. In another recent study, Nasim et al [11] proposed a machine learning-based algorithm for predicting the next call at a particular time.…”

“…For example, a user might start from the call-log only to find that the required alter was not recently contacted and hence might move to contact book to search that alter. Researchers are trying to improve a user's experience when initiating calls by analysing the calling behaviour of users [13][14][15] and some proposed methods to predict the alter that will be contacted at a given time [ 2,[16][17][18][19][20][21]. This prediction is then used to make a short list of alters as a recommendation for the ego with the goal of reducing the effort to find and call a particular alter.…”

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

“…As such the contextual behavioral rules of individual mobile phone users can be used to predict individual's behavior for a certain contextual information. Some examples of such predictions are -to predict the outgoing calls analyzing mobile phone historical call log data [46,30,32] for smart searching in contact list, to predict incoming calls for planning and scheduling (e.g., it can be used to avoid unwanted calls and schedule time for wanted calls) [29], to predict the next mobile application that an individual is going to use for a particular contexts by analyzing individual's app usages data [2,17,50,52], to predict smart phone notification response behavior of individual users utilizing their responses to the notifications stored in the smart phone notification logs, in order to build intelligent notification management system [47,15], to assist them in their daily activities in different situations in a context-aware pervasive computing environment.…”