Model-Based Location Recommender System Using Geotagged Photos On Instagram

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI:10.1109/ICWR49608.2020.9122274

Maryam Memarzadeh, A. Kamandi

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Abstract

Instagram is one of the popular social media services used by a variety of people around the world. It has a huge number of active users. The more users, the larger and the more different Instagram data are available. In this paper, we propose a Model-based location recommender system (MLRS), which creates a profile for each location and uses it to recommend locations, based on user interests. Since our analysis does not have an appropriate dataset to check, we use both Foursquare and Instagram to create our dataset. Next, we propose the Term-Frequency and Inverse Document Frequency(TF-IDF) method to rank extracted hashtags of selected Instagram locations based on Instagram image captions. This gives us the main idea of locations, based on 30 recent image captions hashtag posted. Then, we used FastText to classify hashtags of each location post. We evaluated our system with a large-scale real dataset collected from Instagram concerning precision, recall and the F-measure. Finally, the experimental results show that the highest result achieved when the FastText model tested with n=1 with an F-measure of 77.8%.

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使用Instagram上的地理标记照片的基于模型的位置推荐系统

Instagram是世界各地各种各样的人使用的流行社交媒体服务之一。它拥有大量的活跃用户。用户越多，可用的Instagram数据就越大，种类也就越多。在本文中，我们提出了一个基于模型的位置推荐系统(MLRS)，它为每个位置创建一个配置文件，并根据用户的兴趣使用它来推荐位置。由于我们的分析没有合适的数据集来检查，我们使用Foursquare和Instagram来创建我们的数据集。接下来，我们提出了Term-Frequency和Inverse Document Frequency(TF-IDF)方法，根据Instagram图片标题对所选Instagram位置的提取标签进行排序。根据最近发布的30张图片标题，这给了我们关于地点的主要想法。然后，我们使用FastText对每个位置帖子的标签进行分类。我们用从Instagram收集的大规模真实数据集评估了我们的系统，包括精度、召回率和F-measure。最后，实验结果表明，当n=1时，FastText模型的测试结果最高，f值为77.8%。

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2020 6th International Conference on Web Research (ICWR)

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