Context-Aware Recommendations Using Differential Context Weighting and Metaheuristics

摘要

Context plays a paramount role in language and conversations, and since their incorporation into traditional recommendation engines, which made use of just the user and item details, an effective method to utilize them in the best possible manner is of great importance. In this paper, we propose a novel approach to handle the sparsity of contextual data, their increasing dimensionality, and the development of an effective model for a context-aware recommender system (CARS). We further go on given relevance, in the form of assigning weights even to the individual attributes of each context. Differential context weighting (DCW) is used as the rating model to obtain the desired ratings. Optimization of weights required for DCW is done through metaheuristic techniques, and toward this, we have further gone on to experimentally compare two of the most popular ones, namely particle swarm optimization (PSO) and the firefly algorithm (FA). Recommendations using the optimal one were then obtained.