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A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

机译：基于协作位置的旅行推荐系统，通过增强对用户组的额定值预测

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摘要

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.
机译：Web的快速增长和其应用为推荐系统创造了巨大的重要性。在各个域中应用，推荐系统旨在基于用户兴趣生成项目或服务等建议。基本上，推荐系统体验了许多反映DWINDLED效率的问题。将强大的数据管理技术集成到推荐系统可以解决此类问题，建议质量可以显着提高。最近关于推荐系统的研究揭示了利用社交网络数据来增强传统推荐系统，更好的预测和提高准确性。本文通过考虑使用各种推荐算法，系统的功能，不同类型的接口，过滤技术和人工智能技术，表达了对基于社交网络数据的推荐系统的看法。在检查现有型号的目标深度，方法和数据来源之后，该文件有助于任何对旅行推荐系统开发有兴趣的人，并促进未来的研究方向。我们还提出了一个基于社交相关的信托步行者（SPTW）的位置推荐系统，并将结果与​​现有的基线随机步道模型进行了比较。稍后，我们已经增强了用于一组用户建议的SPTW模型。已经提出了从实验中获得的结果。

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