Predicting Session Length in Media Streaming

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads. In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.Comment: 4 pages, 3 figure

Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking. In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees and naturally incorporates flexible parametric/nonparametric models on the covariates, including models robust to outliers. Our proposal is found to outperform state-of-the-art estimators in terms of efficiency and predictive performance on real world public and private datasets

Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking. In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees and naturally incorporates flexible parametric/nonparametric models on the covariates, including models robust to outliers. Our proposal is found to outperform state-of- the-art estimators in terms of efficiency and predictive performance on real world public and private datasets.Comment: 20 page

Influence Maximization with Fairness at Scale (Extended Version)

In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes are fairly affected, i.e., are proportionally similar between the original network and the affected users. Recent studies on this problem focused only on extremely small networks, hence the challenge remains on how to achieve a scalable solution, applicable to networks with millions or billions of nodes. We propose an approach that is based on learning node representations for fair spread from diffusion cascades, instead of the social connectivity s.t. we can deal with very large graphs. We propose two data-driven approaches: (a) fairness-based participant sampling (FPS), and (b) fairness as context (FAC). Spread related user features, such as the probability of diffusing information to others, are derived from the historical information cascades, using a deep neural network. The extracted features are then used in selecting influencers that maximize the influence spread, while being also fair with respect to the chosen sensitive attributes. In FPS, fairness and cascade length information are considered independently in the decision-making process, while FAC considers these information facets jointly and considers correlations between them. The proposed algorithms are generic and represent the first policy-driven solutions that can be applied to arbitrary sets of sensitive attributes at scale. We evaluate the performance of our solutions on a real-world public dataset (Sina Weibo) and on a hybrid real-synthethic dataset (Digg), which exhibit all the facets that we exploit, namely diffusion network, diffusion traces, and user profiles. These experiments show that our methods outperform the state-the-art solutions in terms of spread, fairness, and scalability

Trade-off between energy consumption and target delay for wireless sensor network

Wireless sensor networks (WSN) consists of unattended sensors with limited storage, energy (battery power) and computational and communication capabilities. Since battery power is the most crucial resource for sensor nodes and delay time is a critical metric for certain WSN applications, data diffusion between source sensors and sink should be done in an energy efficient and timely manner. We characterize the trade off between the energy consumption and source to sink delay in order to extend the operation of individual sensors and hence increase the lifetime of the WSN. To achieve this goal, the transmission range of sensors is first decomposes into certain ranges based on a minimal distance between consecutive forwarding sensors and then classifies these ranges due to Degree of Interest. It is also shown that the use of sensor nodes which lie on or closely to the shortest path between the source and the sink helps minimize these two metrics

Energy efficiency in MAC 802.15.4 for wireless sensor networks

Recent technological advances in sensors, low power integrated circuits, and wireless communications have enabled the design of low-cost, lightweight, and intelligent physiological sensor nodes. The IEEE 802.15.4 is a new wireless personal area network designed for wireless monitoring and control applications. The fast progress of research on energy efficiency in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for this work. In the analysis presented, the star network configuration of 802.15.4 standard at 868 MHz is considered for a Zigbee network. In this paper, we analyze the active duration of the superframe and entered the sleep mode status inside this period. It happens when sensors do not have any data to send. The nonpersistent CSMA uses the adaptive backoff exponent. This method helps the network to be reliable under traffic changes due to save the energy consumption. The introduction of sleep state has shown incredible reduction of the power consumption in all network load changes

Adaptive data collection algorithm for wireless sensor networks

Periodical Data collection from unreachable remote terrain and then transmit information to a base station is one of the targeted application of sensor networks. The energy restriction of battery powered sensor nodes is a big challenge for this network as it is difficult or in some cases not feasible to change the power supply of motes. Therefore, in order to keep the networks operating for long time, efficient utilization of energy is considered with highest priority. In this paper we propose TA-PDC-MAC protocol - a traffic adaptive periodic data collection MAC which is designed in a TDMA fashion. This design is efficient in the ways that it assigns the time slots for nodes’ activity due to their sampling rates in a collision avoidance manner. This ensures minimal consumption of network energy and makes a longer network lifetime, as well as it provides small end-to-end delay and packet loss ratio. Simulation results show that our protocol demonstrates up to 35% better performance than that of most recent protocol that proposed for this kind of application, in respect of energy consumption. Comparative analysis and simulation show that TA-PDC-MAC considerably gives a good compromise between energy efficiency and latency and packet loss rate