Reading information in news feed apps has become a kind of popular content consumption in recent years. However, there are contradictory conclusions about the recommendation strategies. Although some previous research has studied the algorithms to improve the accuracy of the combined of relevancy and diversity strategies, how to compromise them according to user attitude and behavior from individual level is limited. This study aims to solve the dilemma at a theoretical level, and the authors find that the degree of multi-context use is a key boundary. Specifically, 1) the location-based recommendation method may lose effectiveness when the degree of a multi-context is high. 2) The users who prefer to use news feed systems in various contexts in their daily life are more likely to read diverse information. The authors suppose that with the increase of the degree of multi-context use, the location-based recommendation may trigger users perceived privacy threat, which will reduce their satisfaction and participation intention.

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Relevancy or Diversity?: Recommendation Strategy Based on the Degree of Multi-Context Use of News Feed Users

With the popularity of smart phones, plenty of mobile phone applications are developed to meet people's various needs, and mobile application recommendation has become a popular and challenging topic. Most studies focus on learning user preferences from various information both on user-side and APP-side, and recommending based on user similarity or app similarity. However, these methods all have a high probability to cause serious homogenization problems that can not meet users' unknown/new needs. Therefore, recommending diverse apps is more likely to cover users' all preferences, and even guide users to discover new needs and interests. To this end, we give the definition of Application Diversity that taking into account the similarity between apps and the relevance of categories, and propose a novel application recommendation approach that consists of two parts, P-Stair Neural Network (P-SNN) and Dynamic Adjustment Method (DAM). First, P-SNN learns user preferences from multi-dimensional data by using deep neural networks techniques, and predicts users' ratings for uninstalled applications. Then, DAM selects TOP-N applications as the final recommend list with considering both user preferences and recommend diversity. Several experiments on different datasets shows that our algorithm effectively improves the diversity of recommendations in the case of similar accuracy.

A Novel Learning Approach to Improve Mobile Application Recommendation Diversity

With the increasing popularity of mobile phones, constructing user profile from the usage of mobile phone becomes a critical research interest. Previously, the key of these researches was the improvement of the accuracy of user profiling algorithms. However, most of algorithms are hard to achieve theoretical optimum under practical scenario due to the limited performance of consumer-grade terminal. A common solution is to upload raw user data to cloud server and analyze them on the cloud-side, which leads to the huge consumption of cloud computing resources. In this paper, we propose a framework for obtaining user profile based on collaboration between smart terminal devices and cloud servers. The framework divides the computational flow of the user profiling algorithm into two parts, which will be executed on the phone and on the server respectively. The framework reduces the amount of resources consumed on users' phones, while avoiding uploading too much data to the cloud servers. This paper introduces the principle and structure of the framework. Finally, the framework is compared with a terminal-side frame which perform user profile calculations only using terminal devices and a cloud frame obtaining user profile only using cloud servers.

Xiao Xu

Papers

A Novel Learning Approach to Improve Mobile Application Recommendation Diversity

Two-Step User Profiling Framework Based on Terminal-cloud Collaboration