A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

Abstract

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

Location Data Analytics in the Business Value Chain: A Systematic Literature Review

“…Other studies reported satisfactory results with their presented solutions and pointed in the direction of validating those approaches with other real-world data sets [24], [172], and exploring deeply other attributes associated with geotagged events [23], [62]. Furthermore, other authors plan to optimize the training of their models, and/or explore other ad-vanced techniques such as deep learning [67], [140].…”

“…• Behavioral pattern detection: Location context is exploited to detect or infer behavioral patterns among elements of a system. One common use case is exploiting the movement behavior of users to improve mobile services [23]. • Predictive analysis: This problem type is analogous to predictive analytics or supervised learning.…”

“…The study focuses on the inclusion of Geographic Information Systems (GIS) to location selection processes. Another interesting case is the work by Xin et al [23], which elaborates on recommendation systems that exploit user mobile traces.…”

“…Depending on the model objective, there are some metrics that are more suitable than others. We identified the following metrics: 18.00% [23], [24], [28], [46], [58], [66]- [69], [71], [74], [129], [139], [141], [186], [188], [189], [194] Recall 18 18.00% [14], [23], [24], [28], [46], [58], [66]- [69], [71], [72], [74], [129], [139], [141], [186], [188] RMSE 11 11.00% [17], [25], [57]- [59], [62], [141], [158], [164], [171], [172] F1-score 10 10.00% [24], [28], [58], [66], [71],…”

Location Recommendation of Digital Signage Based on Multi-Source Information Fusion

“…Due to the strong correlation between time and geographical location, the study introduced time characteristics into location recommendations and used it to improve the performance of location recommendation [32,33]. In terms of research data, the data sources used in the current location recommendation can be divided into two categories: (1) user information, which includes the profile, location history and t track of users [34][35][36][37]; and (2) characteristics of the location, such as the number of users visiting a certain place and physical properties such as malls and parks [38][39][40]. Although researchers have proposed numerous new methods and models, they have continued to promote the development of location recommendation research.…”