Developing a Contextually Personalized Hybrid Recommender System

Abstract

It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

“…For instance, Swati et al [28] have used KNN technique in their personalizing mobile search engine. Bozanta et al [29] use KNN technique in their analysis while developing a contextually personalized hybrid recommender system. Anagnostopoulos et al [23] use KNN classification technique in their machine learning based analysis while predicting the location of mobile users.…”

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

“…An intelligent interruption management system is proposed in [ 48 ], use decision tree for making decisions. Bozanta et al [ 67 ], Lee et al [ 68 ] use classification technique to build a personalized hybrid recommender system. Turner et al [ 59 , 69 ], Fogarty et al [ 70 ] use classification technique in their interruptibility predictionand management system.…”

Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

“…In addition to the context-aware tree-based models discussed above, several other machine learning classification techniques are used in the area of contextaware computing and smartphone analytics. For instance, Bozanta et al [29] use the k-nearest neighbor classification technique while developing a contextually personalized recommender system. Ayu et al use k-nearest neighbor classification in their study while recognize activity using mobile phone data.…”