A Personalized Hybrid Recommendation Algorithm for Location-Based Service on Smart Campus

Abstract

In order to provide location-based service for teachers and students, this paper proposes a personalized hybrid recommendation algorithm (GTRA) based on user's geographical locations and tags. First, by analyzing the user's historical stay points and the length of stay time in different feature areas, the user's geographical adjacent user set is identified, which well solves scoring matrix sparse problem; then, we propose a tag growth strategy in which club administrators participate to give an effective solution to the problem of cold start; Finally, personalized push service was offered according to the user's identity and the current location. Through the construction of personalized recommendation system and execution of the experiment, the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm, and it is suitable for campus application scenarios, which has some reference to the construction of smart campus in which personalized services for teachers and students are emphasized.

A Systematic Review on Technologies and Applications in Smart Campus: A Human-Centered Case Study

“…AI has been widely used in smart campus applications involving human factor and uncertainties, such as personalized learning, virtual tutoring, decision-making, quantative evaluation, and future condition prediction. In the literature, the commonly used AI techniques include but not limited to facial recognition [26], emotion recognition [33], speech recognition [33], computer vision [34,35], unsupervised learning [36], recommender system [37,38], and anomaly detection [36,39].…”